In [1]:
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:80% !important; }</style>"))

Rent market analysis

Zoe Wang

2018.09.2

Table of Contents

  • 1.1. Get data from different place
    • a. Web API from business.govt.nz
    • b. Get House price CSV file
  • 1.2. Part two Dataset Wrangling
    • a. Check Variable's type
    • b. Check Dimensions
    • c. Check Missing Values
    • d. Variables's Distribution
  • 1.3. Data Preprocessing
    • Convert categorical variables to dummies
  • 1.4. Data Visualization
    • a. House type analysis
    • b. Get relation between house type and bedroom number
    • c. Location number and Distribution
    • d. Mean of rent price
    • e. Boxplot of location and bonds price
    • f. Get mean of rent and number of bonds lodged in 2017 relation by bedrooms type
    • g. Get different location house price in 2017
  • 1.5. Predictive Modeling
    • a. Linear Regression
      • Prediction by Sample Standard Deviation of weekly rent
      • prediction by 5+ Bedroom
    • b. KNN prediction
      • Prediction by Sample Standard Deviation of weekly rent
      • Prediction by Total house price in differet location

Datasets originally and Purpose

During this project i want to do the rent price in different New Zealand area, firstly i find working type, area and some of salary in seek by web scarping. However, their data is not very accurate, and I also face theanti web scraping of seek. so i change my mind to use API by https://api.business.govt.nz/services/v1/tenancy-services/market-rent/statistics I got 30 days permission(from 28/08/2018), which is the rent datasets below. Those datasets come from Tenancy Services. Also i got some datasets frome https://www.qv.co.nz/property-trends/residential-house-values which is about House price from different area of New Zealand

During this research i will discuss those questions:

    1. View the location of the house and the impact of the number of rooms on the rent.
    1. What kind of house is most popular in rent market.
    1. Analysis different location house price.
    1. Try to analyze the relationship between each feature and rent through the obtained datasets.

summary

I tried to find the most suitable predictive analysis model by integrating and analyzing the two data sets, so I used linear regression analysis and knn analysis. When using linear regression, I looked at the correlation of the data in advance, which is very unfortunate. My second dataset did not find a property with a high correlation coefficient, so I used the sample difference data of the rent dataset to analyze it and got a prediction model of r-square at 0.5. I also tried to use the correlation. The higher value of the coefficient 5+ bedroom only yields a prediction model with an r-square of 0.3. At the same time, I brought the properties of these two r-squares into knn for analysis, and obtained two models of KNN regression. I tried different k values and could not get valid predictions.

In linear regression, I also proposed a 95% confidence interval, which helps me to improve the accuracy of the prediction in a normal distribution to an interval value, but because of the incompleteness of the data set, I think it is better than me. The prediction model is very accurate, and I personally think that more comprehensive data information should be collected to get a more reliable prediction model.

Description of attributes:

Wine review:

nLodged - Number of bonds lodged at some point in the period. Note random rounding is applied to this value.

nClosed - Number of bonds closed at some point in the period. Note random rounding is applied to this value.

nCurr - Total number of bonds active at the end of the period. Note random rounding is applied to this value.

mean - Mean weekly rent of bonds lodged within the period.

lq - Lower Quartile weekly rent; weekly rent of the bond that is at the 25th percentile of bonds lodged in the period

med - Median weekly rent; weekly rent of the bond that is at the 50th percentile of bonds lodged in the period

uq - Upper Quartile weekly rent; weekly rent of the bond that is at the 75th percentile of bonds lodged in the period

sd - Sample Standard Deviation of weekly rent

brr - Mean Bond/Rent Ratio

lmean - Mean of natural logarithm weekly rent. Note that exp(lmean) == Geometric mean is a good estimate of the median as rent is log normally distributed so can be thought of as the "Synthetic median" of market rent consistent with the other synthetic statistics below

lsd - sample standard deviation of natural logarithm weekly rent of bonds lodged within the period

slq - Synthetic Lower Quartile Weekly Rent. This is defined as exp(lmean + qnorm(0.25) * lsd) and is a reasonable estimate of the lower quartile

suq - Synthetic Upper Quartile Weekly Rent. This is defined as exp(lmean + qnorm(0.75) * lsd) and is a reasonable estimate of the upper quartile

In [2]:
import pandas as pd
import numpy as np
import matplotlib as mtpl
import matplotlib.pyplot as plt
import seaborn as sns
In [3]:
from pylab import rcParams
rcParams['figure.figsize'] = 15, 10
rcParams['font.size'] = 20
rcParams['figure.dpi'] = 350
rcParams['lines.linewidth'] = 2
rcParams['axes.facecolor'] = 'white'
rcParams['patch.edgecolor'] = 'white'
rcParams['font.family'] = 'StixGeneral'
In [4]:
import os
import sys
import json
import requests,re
import pandas as pd
from nltk import clean_html
from urllib.request import urlopen
from bs4 import BeautifulSoup as bs

Part one Data Acquisition

In [22]:
!pip install requests requests_oauthlib
Requirement already satisfied: requests in e:\anaconda\lib\site-packages (2.18.4)
Requirement already satisfied: requests_oauthlib in e:\anaconda\lib\site-packages (1.0.0)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in e:\anaconda\lib\site-packages (from requests) (3.0.4)
Requirement already satisfied: idna<2.7,>=2.5 in e:\anaconda\lib\site-packages (from requests) (2.6)
Requirement already satisfied: urllib3<1.23,>=1.21.1 in e:\anaconda\lib\site-packages (from requests) (1.22)
Requirement already satisfied: certifi>=2017.4.17 in e:\anaconda\lib\site-packages (from requests) (2018.4.16)
Requirement already satisfied: oauthlib>=0.6.2 in e:\anaconda\lib\site-packages (from requests_oauthlib) (2.1.0)
distributed 1.21.8 requires msgpack, which is not installed.
You are using pip version 10.0.1, however version 18.0 is available.
You should consider upgrading via the 'python -m pip install --upgrade pip' command.
In [23]:
import requests
from requests_oauthlib import OAuth1Session
from requests_oauthlib import OAuth1
In [24]:
#Get earthquake API information
urlrt = "https://api.business.govt.nz/services/v1/tenancy-services/market-rent/statistics?period-ending=2018-06&num-months=12&area-definition=REGC2016&include-aggregates=false"
rent = requests.get(urlrt, headers={'Authorization': 'Bearer 42c167731d734bc78337497f8721ba1d'})
rent
Out[24]:
<Response [200]>
In [25]:
rentinfo = json.loads(rent.content)
rentinfo
Out[25]:
{'items': [{'med': 410,
   'dwell': 'Apartment',
   'lmean': 6.0379,
   'lq': 370,
   'nBedrms': 1,
   'uq': 465,
   'sd': 113,
   'nClosed': 3648,
   'brr': 3.51,
   'slq': 361,
   'suq': 486,
   'nCurr': 5715,
   'area': 'Auckland Region',
   'mean': 430,
   'lsd': 0.2213,
   'nLodged': 3762},
  {'med': 510,
   'dwell': 'Apartment',
   'lmean': 6.2695,
   'lq': 460,
   'nBedrms': 2,
   'uq': 590,
   'sd': 153,
   'nClosed': 3489,
   'brr': 3.55,
   'slq': 451,
   'suq': 619,
   'nCurr': 6195,
   'area': 'Auckland Region',
   'mean': 544,
   'lsd': 0.2345,
   'nLodged': 3699},
  {'med': 625,
   'dwell': 'Apartment',
   'lmean': 6.4657,
   'lq': 540,
   'nBedrms': 3,
   'uq': 750,
   'sd': 262,
   'nClosed': 723,
   'brr': 3.53,
   'slq': 514,
   'suq': 804,
   'nCurr': 1494,
   'area': 'Auckland Region',
   'mean': 681,
   'lsd': 0.3326,
   'nLodged': 675},
  {'med': 610,
   'dwell': 'Apartment',
   'lmean': 6.4805,
   'lq': 595,
   'nBedrms': 4,
   'uq': 640,
   'sd': 150,
   'nClosed': 129,
   'brr': 3.63,
   'slq': 575,
   'suq': 740,
   'nCurr': 207,
   'area': 'Auckland Region',
   'mean': 665,
   'lsd': 0.1863,
   'nLodged': 93},
  {'med': 950,
   'dwell': 'Apartment',
   'lmean': 6.8549,
   'lq': 703,
   'nBedrms': '5+',
   'uq': 1155,
   'sd': 510,
   'nClosed': 18,
   'brr': 3.58,
   'slq': 695,
   'suq': 1295,
   'nCurr': 51,
   'area': 'Auckland Region',
   'mean': 1051,
   'lsd': 0.4611,
   'nLodged': 27},
  {'med': 440,
   'dwell': 'Apartment',
   'lmean': 6.1089,
   'lq': 378,
   'nBedrms': 'NA',
   'uq': 520,
   'sd': 143,
   'nClosed': 1062,
   'brr': 3.75,
   'slq': 375,
   'suq': 540,
   'nCurr': 1275,
   'area': 'Auckland Region',
   'mean': 468,
   'lsd': 0.2714,
   'nLodged': 621},
  {'med': 365,
   'dwell': 'Flat',
   'lmean': 5.8952,
   'lq': 320,
   'nBedrms': 1,
   'uq': 405,
   'sd': 101,
   'nClosed': 1905,
   'brr': 3.35,
   'slq': 312,
   'suq': 423,
   'nCurr': 4938,
   'area': 'Auckland Region',
   'mean': 373,
   'lsd': 0.2244,
   'nLodged': 1785},
  {'med': 450,
   'dwell': 'Flat',
   'lmean': 6.1025,
   'lq': 410,
   'nBedrms': 2,
   'uq': 490,
   'sd': 79,
   'nClosed': 4050,
   'brr': 3.44,
   'slq': 397,
   'suq': 503,
   'nCurr': 11655,
   'area': 'Auckland Region',
   'mean': 454,
   'lsd': 0.1747,
   'nLodged': 3621},
  {'med': 550,
   'dwell': 'Flat',
   'lmean': 6.3094,
   'lq': 490,
   'nBedrms': 3,
   'uq': 600,
   'sd': 154,
   'nClosed': 759,
   'brr': 3.53,
   'slq': 464,
   'suq': 652,
   'nCurr': 2064,
   'area': 'Auckland Region',
   'mean': 567,
   'lsd': 0.2524,
   'nLodged': 960},
  {'med': 680,
   'dwell': 'Flat',
   'lmean': 6.5424,
   'lq': 590,
   'nBedrms': 4,
   'uq': 780,
   'sd': 246,
   'nClosed': 135,
   'brr': 3.57,
   'slq': 578,
   'suq': 833,
   'nCurr': 471,
   'area': 'Auckland Region',
   'mean': 723,
   'lsd': 0.2705,
   'nLodged': 252},
  {'med': 810,
   'dwell': 'Flat',
   'lmean': 6.7317,
   'lq': 750,
   'nBedrms': '5+',
   'uq': 988,
   'sd': 224,
   'nClosed': 24,
   'brr': 3.53,
   'slq': 702,
   'suq': 1001,
   'nCurr': 117,
   'area': 'Auckland Region',
   'mean': 867,
   'lsd': 0.2627,
   'nLodged': 57},
  {'med': 400,
   'dwell': 'Flat',
   'lmean': 6.0254,
   'lq': 323,
   'nBedrms': 'NA',
   'uq': 510,
   'sd': 246,
   'nClosed': 366,
   'brr': 3.36,
   'slq': 311,
   'suq': 551,
   'nCurr': 741,
   'area': 'Auckland Region',
   'mean': 455,
   'lsd': 0.4251,
   'nLodged': 216},
  {'med': 380,
   'dwell': 'House',
   'lmean': 5.9672,
   'lq': 340,
   'nBedrms': 1,
   'uq': 450,
   'sd': 111,
   'nClosed': 486,
   'brr': 3.52,
   'slq': 325,
   'suq': 468,
   'nCurr': 1050,
   'area': 'Auckland Region',
   'mean': 404,
   'lsd': 0.27,
   'nLodged': 507},
  {'med': 470,
   'dwell': 'House',
   'lmean': 6.1691,
   'lq': 425,
   'nBedrms': 2,
   'uq': 530,
   'sd': 114,
   'nClosed': 3669,
   'brr': 3.55,
   'slq': 410,
   'suq': 557,
   'nCurr': 9528,
   'area': 'Auckland Region',
   'mean': 490,
   'lsd': 0.2277,
   'nLodged': 3870},
  {'med': 550,
   'dwell': 'House',
   'lmean': 6.3418,
   'lq': 500,
   'nBedrms': 3,
   'uq': 630,
   'sd': 147,
   'nClosed': 11067,
   'brr': 3.6,
   'slq': 491,
   'suq': 657,
   'nCurr': 32712,
   'area': 'Auckland Region',
   'mean': 582,
   'lsd': 0.2167,
   'nLodged': 11475},
  {'med': 680,
   'dwell': 'House',
   'lmean': 6.5425,
   'lq': 600,
   'nBedrms': 4,
   'uq': 780,
   'sd': 210,
   'nClosed': 5322,
   'brr': 3.66,
   'slq': 588,
   'suq': 819,
   'nCurr': 14232,
   'area': 'Auckland Region',
   'mean': 717,
   'lsd': 0.2457,
   'nLodged': 5385},
  {'med': 800,
   'dwell': 'House',
   'lmean': 6.7223,
   'lq': 730,
   'nBedrms': '5+',
   'uq': 940,
   'sd': 239,
   'nClosed': 1683,
   'brr': 3.69,
   'slq': 703,
   'suq': 982,
   'nCurr': 4875,
   'area': 'Auckland Region',
   'mean': 858,
   'lsd': 0.248,
   'nLodged': 1887},
  {'med': 600,
   'dwell': 'House',
   'lmean': 6.4118,
   'lq': 500,
   'nBedrms': 'NA',
   'uq': 730,
   'sd': 211,
   'nClosed': 1935,
   'brr': 3.54,
   'slq': 494,
   'suq': 751,
   'nCurr': 4266,
   'area': 'Auckland Region',
   'mean': 639,
   'lsd': 0.3101,
   'nLodged': 888},
  {'med': 380,
   'dwell': 'NA',
   'lmean': 5.9153,
   'lq': 320,
   'nBedrms': 1,
   'uq': 420,
   'sd': 119,
   'nClosed': 126,
   'brr': 3.66,
   'slq': 307,
   'suq': 447,
   'nCurr': 237,
   'area': 'Auckland Region',
   'mean': 386,
   'lsd': 0.2781,
   'nLodged': 108},
  {'med': 460,
   'dwell': 'NA',
   'lmean': 6.139,
   'lq': 420,
   'nBedrms': 2,
   'uq': 500,
   'sd': 93,
   'nClosed': 342,
   'brr': 3.65,
   'slq': 409,
   'suq': 526,
   'nCurr': 642,
   'area': 'Auckland Region',
   'mean': 472,
   'lsd': 0.1861,
   'nLodged': 231},
  {'med': 540,
   'dwell': 'NA',
   'lmean': 6.3218,
   'lq': 490,
   'nBedrms': 3,
   'uq': 600,
   'sd': 145,
   'nClosed': 291,
   'brr': 3.72,
   'slq': 478,
   'suq': 648,
   'nCurr': 813,
   'area': 'Auckland Region',
   'mean': 572,
   'lsd': 0.2253,
   'nLodged': 294},
  {'med': 670,
   'dwell': 'NA',
   'lmean': 6.5461,
   'lq': 620,
   'nBedrms': 4,
   'uq': 780,
   'sd': 173,
   'nClosed': 201,
   'brr': 3.68,
   'slq': 596,
   'suq': 814,
   'nCurr': 444,
   'area': 'Auckland Region',
   'mean': 715,
   'lsd': 0.2306,
   'nLodged': 183},
  {'med': 900,
   'dwell': 'NA',
   'lmean': 6.7867,
   'lq': 780,
   'nBedrms': '5+',
   'uq': 1100,
   'sd': 254,
   'nClosed': 96,
   'brr': 3.9,
   'slq': 751,
   'suq': 1045,
   'nCurr': 213,
   'area': 'Auckland Region',
   'mean': 914,
   'lsd': 0.2448,
   'nLodged': 123},
  {'med': 540,
   'dwell': 'NA',
   'lmean': 6.3097,
   'lq': 450,
   'nBedrms': 'NA',
   'uq': 660,
   'sd': 201,
   'nClosed': 4521,
   'brr': 3.67,
   'slq': 444,
   'suq': 680,
   'nCurr': 9582,
   'area': 'Auckland Region',
   'mean': 579,
   'lsd': 0.3155,
   'nLodged': 3744},
  {'med': 210,
   'dwell': 'Room',
   'lmean': 5.4248,
   'lq': 190,
   'nBedrms': 1,
   'uq': 265,
   'sd': 81,
   'nClosed': 1122,
   'brr': 3.33,
   'slq': 189,
   'suq': 273,
   'nCurr': 1227,
   'area': 'Auckland Region',
   'mean': 237,
   'lsd': 0.2719,
   'nLodged': 957},
  {'med': 275,
   'dwell': 'Room',
   'lmean': 5.5388,
   'lq': 191,
   'nBedrms': 'NA',
   'uq': 300,
   'sd': 74,
   'nClosed': 24,
   'brr': 3.14,
   'slq': 210,
   'suq': 308,
   'nCurr': 24,
   'area': 'Auckland Region',
   'mean': 264,
   'lsd': 0.2822,
   'nLodged': 12},
  {'med': 310,
   'dwell': 'Apartment',
   'lmean': 5.7124,
   'lq': 270,
   'nBedrms': 1,
   'uq': 340,
   'sd': 71,
   'nClosed': 81,
   'brr': 3.88,
   'slq': 258,
   'suq': 355,
   'nCurr': 111,
   'area': 'Bay of Plenty Region',
   'mean': 311,
   'lsd': 0.2358,
   'nLodged': 93},
  {'med': 380,
   'dwell': 'Apartment',
   'lmean': 5.9442,
   'lq': 335,
   'nBedrms': 2,
   'uq': 433,
   'sd': 87,
   'nClosed': 150,
   'brr': 3.76,
   'slq': 330,
   'suq': 441,
   'nCurr': 258,
   'area': 'Bay of Plenty Region',
   'mean': 391,
   'lsd': 0.2154,
   'nLodged': 159},
  {'med': 515,
   'dwell': 'Apartment',
   'lmean': 6.2052,
   'lq': 415,
   'nBedrms': 3,
   'uq': 600,
   'sd': 127,
   'nClosed': 75,
   'brr': 3.84,
   'slq': 414,
   'suq': 593,
   'nCurr': 120,
   'area': 'Bay of Plenty Region',
   'mean': 512,
   'lsd': 0.2675,
   'nLodged': 66},
  {'med': 595,
   'dwell': 'Apartment',
   'lmean': 6.4117,
   'lq': 520,
   'nBedrms': 4,
   'uq': 700,
   'sd': 120,
   'nClosed': 6,
   'brr': 3.52,
   'slq': 536,
   'suq': 692,
   'nCurr': 18,
   'area': 'Bay of Plenty Region',
   'mean': 619,
   'lsd': 0.1899,
   'nLodged': 9},
  {'med': 235,
   'dwell': 'Flat',
   'lmean': 5.4791,
   'lq': 200,
   'nBedrms': 1,
   'uq': 300,
   'sd': 73,
   'nClosed': 264,
   'brr': 3.64,
   'slq': 198,
   'suq': 291,
   'nCurr': 669,
   'area': 'Bay of Plenty Region',
   'mean': 250,
   'lsd': 0.2857,
   'nLodged': 258},
  {'med': 300,
   'dwell': 'Flat',
   'lmean': 5.6973,
   'lq': 250,
   'nBedrms': 2,
   'uq': 360,
   'sd': 81,
   'nClosed': 594,
   'brr': 3.76,
   'slq': 247,
   'suq': 360,
   'nCurr': 1617,
   'area': 'Bay of Plenty Region',
   'mean': 309,
   'lsd': 0.2805,
   'nLodged': 531},
  {'med': 393,
   'dwell': 'Flat',
   'lmean': 5.8833,
   'lq': 300,
   'nBedrms': 3,
   'uq': 450,
   'sd': 103,
   'nClosed': 90,
   'brr': 3.64,
   'slq': 286,
   'suq': 450,
   'nCurr': 255,
   'area': 'Bay of Plenty Region',
   'mean': 377,
   'lsd': 0.3356,
   'nLodged': 120},
  {'med': 470,
   'dwell': 'Flat',
   'lmean': 6.0194,
   'lq': 324,
   'nBedrms': 4,
   'uq': 540,
   'sd': 137,
   'nClosed': 18,
   'brr': 3.89,
   'slq': 320,
   'suq': 528,
   'nCurr': 39,
   'area': 'Bay of Plenty Region',
   'mean': 436,
   'lsd': 0.3702,
   'nLodged': 21},
  {'med': 270,
   'dwell': 'Flat',
   'lmean': 5.6516,
   'lq': 220,
   'nBedrms': 'NA',
   'uq': 345,
   'sd': 101,
   'nClosed': 36,
   'brr': 3.49,
   'slq': 231,
   'suq': 351,
   'nCurr': 96,
   'area': 'Bay of Plenty Region',
   'mean': 299,
   'lsd': 0.309,
   'nLodged': 27},
  {'med': 278,
   'dwell': 'House',
   'lmean': 5.5992,
   'lq': 226,
   'nBedrms': 1,
   'uq': 320,
   'sd': 110,
   'nClosed': 84,
   'brr': 3.7,
   'slq': 213,
   'suq': 342,
   'nCurr': 192,
   'area': 'Bay of Plenty Region',
   'mean': 287,
   'lsd': 0.3501,
   'nLodged': 96},
  {'med': 360,
   'dwell': 'House',
   'lmean': 5.813,
   'lq': 300,
   'nBedrms': 2,
   'uq': 400,
   'sd': 110,
   'nClosed': 846,
   'brr': 3.72,
   'slq': 264,
   'suq': 424,
   'nCurr': 2397,
   'area': 'Bay of Plenty Region',
   'mean': 352,
   'lsd': 0.35,
   'nLodged': 864},
  {'med': 420,
   'dwell': 'House',
   'lmean': 5.9895,
   'lq': 360,
   'nBedrms': 3,
   'uq': 480,
   'sd': 98,
   'nClosed': 3174,
   'brr': 3.79,
   'slq': 329,
   'suq': 485,
   'nCurr': 8043,
   'area': 'Bay of Plenty Region',
   'mean': 414,
   'lsd': 0.2884,
   'nLodged': 2829},
  {'med': 520,
   'dwell': 'House',
   'lmean': 6.1974,
   'lq': 450,
   'nBedrms': 4,
   'uq': 565,
   'sd': 105,
   'nClosed': 813,
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   'dwell': 'House',
   'lmean': 6.3295,
   'lq': 500,
   'nBedrms': '5+',
   'uq': 630,
   'sd': 117,
   'nClosed': 345,
   'brr': 3.82,
   'slq': 483,
   'suq': 652,
   'nCurr': 813,
   'area': 'Waikato Region',
   'mean': 574,
   'lsd': 0.2222,
   'nLodged': 327},
  {'med': 420,
   'dwell': 'House',
   'lmean': 6.0217,
   'lq': 350,
   'nBedrms': 'NA',
   'uq': 520,
   'sd': 113,
   'nClosed': 477,
   'brr': 3.69,
   'slq': 339,
   'suq': 502,
   'nCurr': 1098,
   'area': 'Waikato Region',
   'mean': 429,
   'lsd': 0.292,
   'nLodged': 267},
  {'med': 250,
   'dwell': 'NA',
   'lmean': 5.5019,
   'lq': 220,
   'nBedrms': 1,
   'uq': 260,
   'sd': 58,
   'nClosed': 36,
   'brr': 3.87,
   'slq': 212,
   'suq': 284,
   'nCurr': 48,
   'area': 'Waikato Region',
   'mean': 251,
   'lsd': 0.2166,
   'nLodged': 45},
  {'med': 310,
   'dwell': 'NA',
   'lmean': 5.6802,
   'lq': 243,
   'nBedrms': 2,
   'uq': 350,
   'sd': 78,
   'nClosed': 51,
   'brr': 3.82,
   'slq': 243,
   'suq': 353,
   'nCurr': 105,
   'area': 'Waikato Region',
   'mean': 304,
   'lsd': 0.2769,
   'nLodged': 48},
  {'med': 400,
   'dwell': 'NA',
   'lmean': 5.9466,
   'lq': 350,
   'nBedrms': 3,
   'uq': 450,
   'sd': 74,
   'nClosed': 114,
   'brr': 3.82,
   'slq': 330,
   'suq': 443,
   'nCurr': 192,
   'area': 'Waikato Region',
   'mean': 391,
   'lsd': 0.2183,
   'nLodged': 93},
  {'med': 500,
   'dwell': 'NA',
   'lmean': 6.1753,
   'lq': 475,
   'nBedrms': 4,
   'uq': 550,
   'sd': 80,
   'nClosed': 66,
   'brr': 3.71,
   'slq': 420,
   'suq': 551,
   'nCurr': 99,
   'area': 'Waikato Region',
   'mean': 489,
   'lsd': 0.2014,
   'nLodged': 54},
  {'med': 485,
   'dwell': 'NA',
   'lmean': 6.0578,
   'lq': 410,
   'nBedrms': '5+',
   'uq': 610,
   'sd': 177,
   'nClosed': 12,
   'brr': 3.99,
   'slq': 304,
   'suq': 602,
   'nCurr': 24,
   'area': 'Waikato Region',
   'mean': 470,
   'lsd': 0.5069,
   'nLodged': 9},
  {'med': 380,
   'dwell': 'NA',
   'lmean': 5.8942,
   'lq': 310,
   'nBedrms': 'NA',
   'uq': 440,
   'sd': 99,
   'nClosed': 525,
   'brr': 3.88,
   'slq': 299,
   'suq': 440,
   'nCurr': 1035,
   'area': 'Waikato Region',
   'mean': 377,
   'lsd': 0.2869,
   'nLodged': 288},
  {'med': 225,
   'dwell': 'Room',
   'lmean': 5.3051,
   'lq': 175,
   'nBedrms': 1,
   'uq': 237,
   'sd': 56,
   'nClosed': 270,
   'brr': 3.75,
   'slq': 164,
   'suq': 247,
   'nCurr': 309,
   'area': 'Waikato Region',
   'mean': 209,
   'lsd': 0.3003,
   'nLodged': 228},
  {'med': 370,
   'dwell': 'Apartment',
   'lmean': 5.9254,
   'lq': 320,
   'nBedrms': 1,
   'uq': 450,
   'sd': 97,
   'nClosed': 912,
   'brr': 3.23,
   'slq': 316,
   'suq': 444,
   'nCurr': 1494,
   'area': 'Wellington Region',
   'mean': 386,
   'lsd': 0.2523,
   'nLodged': 1053},
  {'med': 515,
   'dwell': 'Apartment',
   'lmean': 6.2366,
   'lq': 440,
   'nBedrms': 2,
   'uq': 600,
   'sd': 149,
   'nClosed': 993,
   'brr': 3.28,
   'slq': 427,
   'suq': 612,
   'nCurr': 1920,
   'area': 'Wellington Region',
   'mean': 530,
   'lsd': 0.2668,
   'nLodged': 963},
  {'med': 640,
   'dwell': 'Apartment',
   'lmean': 6.4466,
   'lq': 560,
   'nBedrms': 3,
   'uq': 740,
   'sd': 191,
   'nClosed': 285,
   'brr': 3.23,
   'slq': 503,
   'suq': 790,
   'nCurr': 555,
   'area': 'Wellington Region',
   'mean': 661,
   'lsd': 0.335,
   'nLodged': 273},
  {'med': 800,
   'dwell': 'Apartment',
   'lmean': 6.6531,
   'lq': 725,
   'nBedrms': 4,
   'uq': 873,
   'sd': 200,
   'nClosed': 114,
   'brr': 3.28,
   'slq': 638,
   'suq': 942,
   'nCurr': 189,
   'area': 'Wellington Region',
   'mean': 803,
   'lsd': 0.2889,
   'nLodged': 105},
  {'med': 865,
   'dwell': 'Apartment',
   'lmean': 6.296,
   'lq': 180,
   'nBedrms': '5+',
   'uq': 1050,
   'sd': 433,
   'nClosed': 129,
   'brr': 3.3,
   'slq': 307,
   'suq': 957,
   'nCurr': 189,
   'area': 'Wellington Region',
   'mean': 719,
   'lsd': 0.8422,
   'nLodged': 141},
  {'med': 365,
   'dwell': 'Apartment',
   'lmean': 6.0075,
   'lq': 335,
   'nBedrms': 'NA',
   'uq': 506,
   'sd': 229,
   'nClosed': 117,
   'brr': 3.2,
   'slq': 309,
   'suq': 535,
   'nCurr': 189,
   'area': 'Wellington Region',
   'mean': 445,
   'lsd': 0.4081,
   'nLodged': 78},
  {'med': 310,
   'dwell': 'Flat',
   'lmean': 5.7181,
   'lq': 260,
   'nBedrms': 1,
   'uq': 360,
   'sd': 82,
   'nClosed': 1062,
   'brr': 3.26,
   'slq': 254,
   'suq': 364,
   'nCurr': 2634,
   'area': 'Wellington Region',
   'mean': 315,
   'lsd': 0.2659,
   'nLodged': 924},
  {'med': 380,
   'dwell': 'Flat',
   'lmean': 5.9306,
   'lq': 320,
   'nBedrms': 2,
   'uq': 440,
   'sd': 95,
   'nClosed': 1371,
   'brr': 3.32,
   'slq': 319,
   'suq': 444,
   'nCurr': 3855,
   'area': 'Wellington Region',
   'mean': 388,
   'lsd': 0.2462,
   'nLodged': 1161},
  {'med': 500,
   'dwell': 'Flat',
   'lmean': 6.2133,
   'lq': 429,
   'nBedrms': 3,
   'uq': 600,
   'sd': 136,
   'nClosed': 441,
   'brr': 3.32,
   'slq': 414,
   'suq': 603,
   'nCurr': 1113,
   'area': 'Wellington Region',
   'mean': 518,
   'lsd': 0.2786,
   'nLodged': 474},
  {'med': 720,
   'dwell': 'Flat',
   'lmean': 6.5053,
   'lq': 590,
   'nBedrms': 4,
   'uq': 840,
   'sd': 187,
   'nClosed': 93,
   'brr': 3.29,
   'slq': 540,
   'suq': 827,
   'nCurr': 258,
   'area': 'Wellington Region',
   'mean': 698,
   'lsd': 0.3156,
   'nLodged': 132},
  {'med': 950,
   'dwell': 'Flat',
   'lmean': 6.6552,
   'lq': 715,
   'nBedrms': '5+',
   'uq': 1100,
   'sd': 381,
   'nClosed': 57,
   'brr': 3.2,
   'slq': 517,
   'suq': 1167,
   'nCurr': 93,
   'area': 'Wellington Region',
   'mean': 890,
   'lsd': 0.6035,
   'nLodged': 57},
  {'med': 350,
   'dwell': 'Flat',
   'lmean': 5.8952,
   'lq': 320,
   'nBedrms': 'NA',
   'uq': 400,
   'sd': 152,
   'nClosed': 162,
   'brr': 3.02,
   'slq': 295,
   'suq': 447,
   'nCurr': 330,
   'area': 'Wellington Region',
   'mean': 383,
   'lsd': 0.3085,
   'nLodged': 105},
  {'med': 350,
   'dwell': 'House',
   'lmean': 5.8387,
   'lq': 290,
   'nBedrms': 1,
   'uq': 400,
   'sd': 121,
   'nClosed': 192,
   'brr': 3.26,
   'slq': 275,
   'suq': 428,
   'nCurr': 444,
   'area': 'Wellington Region',
   'mean': 362,
   'lsd': 0.3265,
   'nLodged': 189},
  {'med': 420,
   'dwell': 'House',
   'lmean': 6.0306,
   'lq': 350,
   'nBedrms': 2,
   'uq': 495,
   'sd': 119,
   'nClosed': 1428,
   'brr': 3.32,
   'slq': 346,
   'suq': 500,
   'nCurr': 3795,
   'area': 'Wellington Region',
   'mean': 431,
   'lsd': 0.2721,
   'nLodged': 1497},
  {'med': 490,
   'dwell': 'House',
   'lmean': 6.1839,
   'lq': 406,
   'nBedrms': 3,
   'uq': 590,
   'sd': 141,
   'nClosed': 3354,
   'brr': 3.45,
   'slq': 401,
   'suq': 586,
   'nCurr': 9315,
   'area': 'Wellington Region',
   'mean': 504,
   'lsd': 0.2817,
   'nLodged': 3180},
  {'med': 650,
   'dwell': 'House',
   'lmean': 6.4361,
   'lq': 530,
   'nBedrms': 4,
   'uq': 750,
   'sd': 187,
   'nClosed': 1263,
   'brr': 3.5,
   'slq': 504,
   'suq': 773,
   'nCurr': 3069,
   'area': 'Wellington Region',
   'mean': 652,
   'lsd': 0.3176,
   'nLodged': 1206},
  {'med': 870,
   'dwell': 'House',
   'lmean': 6.7266,
   'lq': 685,
   'nBedrms': '5+',
   'uq': 1090,
   'sd': 306,
   'nClosed': 297,
   'brr': 3.41,
   'slq': 647,
   'suq': 1077,
   'nCurr': 699,
   'area': 'Wellington Region',
   'mean': 890,
   'lsd': 0.3779,
   'nLodged': 315},
  {'med': 550,
   'dwell': 'House',
   'lmean': 6.3014,
   'lq': 400,
   'nBedrms': 'NA',
   'uq': 750,
   'sd': 258,
   'nClosed': 393,
   'brr': 3.29,
   'slq': 409,
   'suq': 727,
   'nCurr': 915,
   'area': 'Wellington Region',
   'mean': 596,
   'lsd': 0.4258,
   'nLodged': 141},
  {'med': 300,
   'dwell': 'NA',
   'lmean': 5.6905,
   'lq': 250,
   'nBedrms': 1,
   'uq': 350,
   'sd': 97,
   'nClosed': 93,
   'brr': 3.75,
   'slq': 239,
   'suq': 367,
   'nCurr': 132,
   'area': 'Wellington Region',
   'mean': 311,
   'lsd': 0.3166,
   'nLodged': 96},
  {'med': 380,
   'dwell': 'NA',
   'lmean': 5.9714,
   'lq': 320,
   'nBedrms': 2,
   'uq': 480,
   'sd': 113,
   'nClosed': 108,
   'brr': 3.81,
   'slq': 328,
   'suq': 468,
   'nCurr': 240,
   'area': 'Wellington Region',
   'mean': 406,
   'lsd': 0.263,
   'nLodged': 135},
  {'med': 490,
   'dwell': 'NA',
   'lmean': 6.1838,
   'lq': 400,
   'nBedrms': 3,
   'uq': 570,
   'sd': 113,
   'nClosed': 102,
   'brr': 3.75,
   'slq': 416,
   'suq': 565,
   'nCurr': 228,
   'area': 'Wellington Region',
   'mean': 497,
   'lsd': 0.2264,
   'nLodged': 108},
  {'med': 655,
   'dwell': 'NA',
   'lmean': 6.4743,
   'lq': 550,
   'nBedrms': 4,
   'uq': 746,
   'sd': 210,
   'nClosed': 39,
   'brr': 3.62,
   'slq': 537,
   'suq': 782,
   'nCurr': 84,
   'area': 'Wellington Region',
   'mean': 675,
   'lsd': 0.2784,
   'nLodged': 42},
  {'med': 775,
   'dwell': 'NA',
   'lmean': 6.3758,
   'lq': 440,
   'nBedrms': '5+',
   'uq': 938,
   'sd': 385,
   'nClosed': 18,
   'brr': 3.69,
   'slq': 351,
   'suq': 983,
   'nCurr': 33,
   'area': 'Wellington Region',
   'mean': 728,
   'lsd': 0.7625,
   'nLodged': 21},
  {'med': 460,
   'dwell': 'NA',
   'lmean': 6.1599,
   'lq': 368,
   'nBedrms': 'NA',
   'uq': 620,
   'sd': 223,
   'nClosed': 450,
   'brr': 3.55,
   'slq': 356,
   'suq': 630,
   'nCurr': 981,
   'area': 'Wellington Region',
   'mean': 516,
   'lsd': 0.4237,
   'nLodged': 213},
  {'med': 185,
   'dwell': 'Room',
   'lmean': 5.2969,
   'lq': 170,
   'nBedrms': 1,
   'uq': 235,
   'sd': 61,
   'nClosed': 702,
   'brr': 3.07,
   'slq': 167,
   'suq': 238,
   'nCurr': 762,
   'area': 'Wellington Region',
   'mean': 207,
   'lsd': 0.2631,
   'nLodged': 594},
  {'med': 215,
   'dwell': 'Room',
   'lmean': 5.3575,
   'lq': 178,
   'nBedrms': 'NA',
   'uq': 260,
   'sd': 53,
   'nClosed': 12,
   'brr': 2.59,
   'slq': 179,
   'suq': 251,
   'nCurr': 18,
   'area': 'Wellington Region',
   'mean': 218,
   'lsd': 0.25,
   'nLodged': 9},
  {'med': 135,
   'dwell': 'Flat',
   'lmean': 5.0166,
   'lq': 130,
   'nBedrms': 1,
   'uq': 180,
   'sd': 30,
   'nClosed': 24,
   'brr': 3.18,
   'slq': 133,
   'suq': 171,
   'nCurr': 60,
   'area': 'West Coast Region',
   'mean': 154,
   'lsd': 0.1848,
   'nLodged': 39},
  {'med': 200,
   'dwell': 'Flat',
   'lmean': 5.3031,
   'lq': 175,
   'nBedrms': 2,
   'uq': 240,
   'sd': 44,
   'nClosed': 66,
   'brr': 3.23,
   'slq': 173,
   'suq': 233,
   'nCurr': 129,
   'area': 'West Coast Region',
   'mean': 206,
   'lsd': 0.2219,
   'nLodged': 63},
  {'med': 250,
   'dwell': 'Flat',
   'lmean': 5.558,
   'lq': 250,
   'nBedrms': 3,
   'uq': 280,
   'sd': 88,
   'nClosed': 6,
   'brr': 3.48,
   'slq': 210,
   'suq': 321,
   'nCurr': 12,
   'area': 'West Coast Region',
   'mean': 271,
   'lsd': 0.316,
   'nLodged': 9},
  {'med': 230,
   'dwell': 'House',
   'lmean': 5.3976,
   'lq': 200,
   'nBedrms': 2,
   'uq': 250,
   'sd': 45,
   'nClosed': 90,
   'brr': 3.23,
   'slq': 192,
   'suq': 255,
   'nCurr': 165,
   'area': 'West Coast Region',
   'mean': 226,
   'lsd': 0.2105,
   'nLodged': 93},
  {'med': 250,
   'dwell': 'House',
   'lmean': 5.5289,
   'lq': 220,
   'nBedrms': 3,
   'uq': 300,
   'sd': 62,
   'nClosed': 270,
   'brr': 3.48,
   'slq': 215,
   'suq': 295,
   'nCurr': 579,
   'area': 'West Coast Region',
   'mean': 259,
   'lsd': 0.2322,
   'nLodged': 243},
  {'med': 300,
   'dwell': 'House',
   'lmean': 5.6807,
   'lq': 250,
   'nBedrms': 4,
   'uq': 340,
   'sd': 60,
   'nClosed': 51,
   'brr': 3.47,
   'slq': 255,
   'suq': 338,
   'nCurr': 153,
   'area': 'West Coast Region',
   'mean': 299,
   'lsd': 0.2089,
   'nLodged': 51},
  {'med': 250,
   'dwell': 'House',
   'lmean': 5.595,
   'lq': 240,
   'nBedrms': 'NA',
   'uq': 290,
   'sd': 51,
   'nClosed': 24,
   'brr': 3.32,
   'slq': 239,
   'suq': 303,
   'nCurr': 48,
   'area': 'West Coast Region',
   'mean': 273,
   'lsd': 0.1778,
   'nLodged': 9},
  {'med': 280,
   'dwell': 'NA',
   'lmean': 5.516,
   'lq': 215,
   'nBedrms': 'NA',
   'uq': 310,
   'sd': 87,
   'nClosed': 18,
   'brr': 2.76,
   'slq': 195,
   'suq': 317,
   'nCurr': 27,
   'area': 'West Coast Region',
   'mean': 263,
   'lsd': 0.3607,
   'nLodged': 9},
  {'med': 90,
   'dwell': 'Room',
   'lmean': 4.4998,
   'lq': 90,
   'nBedrms': 1,
   'uq': 90,
   'sd': None,
   'nClosed': 9,
   'brr': 3.33,
   'slq': 90,
   'suq': 90,
   'nCurr': 6,
   'area': 'West Coast Region',
   'mean': 90,
   'lsd': 0.0,
   'nLodged': 12}],
 'periodCovered': '2017-7-1/2018-6-30',
 'areaDefinition': 'REGC2016'}
In [26]:
rent_info = []
for i in range(len(rentinfo['items'])): 
        rent_info.append({
            'Location':rentinfo['items'][i]['area'],
            'Houeing Type':rentinfo['items'][i]['dwell'],
            'Number of Bedrooms':rentinfo['items'][i]['nBedrms'],
            'Mean of rent':rentinfo['items'][i]['mean'],
            'lmean':rentinfo['items'][i]['lmean'],
            'lq':rentinfo['items'][i]['lq'],
            'uq':rentinfo['items'][i]['uq'],
            'sd':rentinfo['items'][i]['sd'],
            'brr':rentinfo['items'][i]['brr'],
            'slq':rentinfo['items'][i]['slq'],
            'suq':rentinfo['items'][i]['suq'],
            'nCurr':rentinfo['items'][i]['nCurr'],
            'lsd':rentinfo['items'][i]['lsd'],
            'nLodged':rentinfo['items'][i]['nLodged']
            
        })
        rent = pd.DataFrame(rent_info)
In [27]:
rent.head()
Out[27]:
Houeing Type Location Mean of rent Number of Bedrooms brr lmean lq lsd nCurr nLodged sd slq suq uq
0 Apartment Auckland Region 430 1 3.51 6.0379 370 0.2213 5715 3762 113.0 361 486 465
1 Apartment Auckland Region 544 2 3.55 6.2695 460 0.2345 6195 3699 153.0 451 619 590
2 Apartment Auckland Region 681 3 3.53 6.4657 540 0.3326 1494 675 262.0 514 804 750
3 Apartment Auckland Region 665 4 3.63 6.4805 595 0.1863 207 93 150.0 575 740 640
4 Apartment Auckland Region 1051 5+ 3.58 6.8549 703 0.4611 51 27 510.0 695 1295 1155
In [28]:
#Get House price during 2017.07-2018.05
hsh1 = pd.read_csv('.../datasets/All-2018-08-29 102400324.csv')
hsh2 = pd.read_csv('.../datasets/All-2018-08-29 102506553.csv')
hsh3 = pd.read_csv('.../datasets/All-2018-08-29 102517261.csv')
hsh4 = pd.read_csv('.../datasets/All-2018-08-29 102638289.csv')
hsh5 = pd.read_csv('.../datasets/All-2018-08-29 102648559.csv')
hsh6 = pd.read_csv('.../datasets/All-2018-08-29 102700412.csv')
hsh7 = pd.read_csv('.../datasets/All-2018-08-29 102740339.csv')
hsh8 = pd.read_csv('.../datasets/All-2018-08-29 102751571.csv')
hsh9 = pd.read_csv('.../datasets/All-2018-08-29 102759493.csv')
hsh10 = pd.read_csv('.../datasets/All-2018-08-29 102808165.csv')
hsh11 = pd.read_csv('.../datasets/All-2018-08-29 102818251.csv')
hsh12 = pd.read_csv('.../datasets/All-2018-08-29 102826954.csv')
hsh13 = pd.read_csv('.../datasets/All-2018-08-29 102837189.csv')
hsh14 = pd.read_csv('.../datasets/All-2018-08-29 131439302.csv')
#Merge ever month house price dataframe
house_df1 = pd.merge(hsh1, hsh2, on='Area', suffixes=('_Jan', '_Feb'))
house_df2 = pd.merge(hsh3, hsh4, on='Area', suffixes=('_Mar', '_Apr'))
house_df3 = pd.merge(hsh5, hsh6, on='Area', suffixes=('_May', '_Jul'))
house_df4 = pd.merge(hsh7, hsh8, on='Area', suffixes=('_Jun', '_Aug'))
house_df5 = pd.merge(hsh9, hsh10, on='Area', suffixes=('_Sep', '_Oct'))
house_df6 = pd.merge(hsh11, hsh12, on='Area', suffixes=('_Nov', '_Dce'))        
In [29]:
#Merge month dataframe
house_price1 = pd.merge(house_df1, house_df2, on='Area')
house_price2 = pd.merge(house_df3, house_df4, on='Area')
house_price3 = pd.merge(house_df5, house_df6, on='Area')
house_price4 = pd.merge(house_price1, house_price2, on='Area')
house_price5 = pd.merge(house_price3, hsh14, on='Area')
house_price = pd.merge(house_price4, house_price5, on='Area')
house_price = pd.DataFrame(data=house_price )
house_price.head()
Out[29]:
Area Average value January 2018 Average value January 2017 Change value_Jan Average value February 2018 Average value February 2017 Change value_Feb Average value March 2018 Average value March 2017 Change value_Mar ... Change value_Oct Average value October 2017 Average value October 2016 Change value_Nov Average value November 2017 Average value November 2016 Change value_Dce Average value December 2017 Average value December 2016 Change value
0 New Zealand $671,531 $631,302 6.4% $672,645 $631,349 6.5% $677,618 $631,432 7.3% ... 4.3% $646,807 $622,309 3.9% $664,698 $624,675 6.4% $669,565 $627,905 6.6%
1 Main Urban Areas $787,740 $754,572 4.4% $788,173 $750,253 5.1% $793,788 $748,957 6.0% ... 2.1% $759,526 $747,507 1.6% $779,380 $751,113 3.8% $786,246 $751,460 4.6%
2 Auckland Area $1,054,974 $1,047,699 0.7% $1,053,948 $1,043,680 1.0% $1,055,992 $1,045,362 1.0% ... 0.8% $1,038,722 $1,045,207 -0.6% $1,045,741 $1,051,387 -0.5% $1,051,762 $1,047,179 0.4%
3 Wellington Area $634,811 $582,322 9.0% $640,737 $589,784 8.6% $644,567 $595,501 8.2% ... 9.6% $610,579 $558,886 9.2% $621,289 $565,631 9.8% $628,450 $574,410 9.4%
4 Far North District $421,197 $389,811 8.1% $427,406 $399,780 6.9% $431,570 $397,600 8.5% ... 10.5% $421,696 $359,876 17.2% $420,783 $367,005 14.7% $421,582 $376,947 11.8%

5 rows × 40 columns

In [30]:
#Remove '$',',' and '%' in dataframe
for i in house_price:
    house_price[i] = house_price[i].str.lstrip('$').str.rstrip('%').str.replace(',', '')
In [31]:
#Rename Area to Location
house_price = house_price.rename(columns={'Area':'Location'})
house_price = house_price.rename(columns={'Average value June 2017_x':'Average value June 2017'})
In [32]:
#Get 2017 house price  
houe_price_2017 = house_price.drop(['Average value January 2018','Change value_Jan','Average value February 2018', 'Change value_Feb', 'Change value_Feb','Average value March 2018','Average value April 2018','Change value_Apr','Average value May 2018','Change value_May','Average value June 2016','Average value June 2018','Change value_Jun','Average value June 2017_y','Change value_Jul','Average value July 2016','Change value_Aug','Average value August 2016','Change value_Sep','Average value September 2016','Change value_Oct','Average value October 2016','Change value_Nov','Average value November 2016','Average value December 2016','Change value','Change value_Mar','Change value_Dce'], axis=1)
houe_price_2017.head()
Out[32]:
Location Average value January 2017 Average value February 2017 Average value March 2017 Average value April 2017 Average value May 2017 Average value June 2017 Average value July 2017 Average value August 2017 Average value September 2017 Average value October 2017 Average value November 2017 Average value December 2017
0 New Zealand 631302 631349 631432 631147 634018 639051 641280 641648 646378 646807 664698 669565
1 Main Urban Areas 754572 750253 748957 746641 748592 751563 753271 752123 758639 759526 779380 786246
2 Auckland Area 1047699 1043680 1045362 1043830 1044561 1045059 1044303 1041957 1039066 1038722 1045741 1051762
3 Wellington Area 582322 589784 595501 602230 607907 609552 607011 605435 606322 610579 621289 628450
4 Far North District 389811 399780 397600 401968 402966 408416 405772 413417 409120 421696 420783 421582

Part two Dataset Wrangling

这个部分我想合并所有的相关数据并处理确实数据值

Check Variable's type

In [33]:
rent.dtypes
Out[33]:
Houeing Type           object
Location               object
Mean of rent            int64
Number of Bedrooms     object
brr                   float64
lmean                 float64
lq                      int64
lsd                   float64
nCurr                   int64
nLodged                 int64
sd                    float64
slq                     int64
suq                     int64
uq                      int64
dtype: object
In [34]:
#Change house price data type frome object to float
#house_price[['Average value January 2018']]=pd.DataFrame(house_price[['Average value January 2018']],dtype=np.float)
#house_price[['Average value January 2018','Average value January 2017','Change value_Jan']] = house_price[['Average value January 2018','Average value January 2017','Change value_Jan']].apply(pd.to_numeric)
house_price = house_price.apply(pd.to_numeric, errors='ignore')
houe_price_2017 = houe_price_2017.apply(pd.to_numeric, errors='ignore')
In [35]:
house_price.dtypes
Out[35]:
Location                         object
Average value January 2018        int64
Average value January 2017        int64
Change value_Jan                float64
Average value February 2018       int64
Average value February 2017       int64
Change value_Feb                float64
Average value March 2018          int64
Average value March 2017          int64
Change value_Mar                float64
Average value April 2018          int64
Average value April 2017          int64
Change value_Apr                float64
Average value May 2018            int64
Average value May 2017            int64
Change value_May                float64
Average value June 2018           int64
Average value June 2017           int64
Change value_Jul                float64
Average value June 2017_y         int64
Average value June 2016           int64
Change value_Jun                float64
Average value July 2017           int64
Average value July 2016           int64
Change value_Aug                float64
Average value August 2017         int64
Average value August 2016         int64
Change value_Sep                float64
Average value September 2017      int64
Average value September 2016      int64
Change value_Oct                float64
Average value October 2017        int64
Average value October 2016        int64
Change value_Nov                float64
Average value November 2017       int64
Average value November 2016       int64
Change value_Dce                float64
Average value December 2017       int64
Average value December 2016       int64
Change value                    float64
dtype: object

Check Dimensions

In [36]:
rent.shape
Out[36]:
(341, 14)
In [37]:
house_price.shape
Out[37]:
(105, 40)

Check Missing Values

In [38]:
rent.isnull().sum()
Out[38]:
Houeing Type          0
Location              0
Mean of rent          0
Number of Bedrooms    0
brr                   0
lmean                 0
lq                    0
lsd                   0
nCurr                 0
nLodged               0
sd                    1
slq                   0
suq                   0
uq                    0
dtype: int64
In [39]:
houe_price_2017.isnull().sum()
Out[39]:
Location                        0
Average value January 2017      0
Average value February 2017     0
Average value March 2017        0
Average value April 2017        0
Average value May 2017          0
Average value June 2017         0
Average value July 2017         0
Average value August 2017       0
Average value September 2017    0
Average value October 2017      0
Average value November 2017     0
Average value December 2017     0
dtype: int64

Variables's Distribution

In [40]:
rent.describe(include=[np.number])
Out[40]:
Mean of rent brr lmean lq lsd nCurr nLodged sd slq suq uq
count 341.000000 341.000000 341.000000 341.000000 341.000000 341.000000 341.000000 340.000000 341.000000 341.000000 341.000000
mean 396.170088 3.560733 5.868954 331.445748 0.263284 1084.741935 476.788856 109.838235 317.519062 458.304985 446.366569
std 159.023843 0.283621 0.383146 131.010510 0.102218 2814.833235 1192.188431 75.075421 119.948311 193.360987 186.647979
min 90.000000 2.300000 4.499800 90.000000 0.000000 0.000000 6.000000 9.000000 87.000000 90.000000 90.000000
25% 282.000000 3.410000 5.595500 235.000000 0.201300 48.000000 21.000000 61.000000 226.000000 321.000000 314.000000
50% 371.000000 3.610000 5.883400 315.000000 0.248600 189.000000 81.000000 88.000000 302.000000 425.000000 405.000000
75% 470.000000 3.750000 6.123900 400.000000 0.315500 792.000000 306.000000 137.000000 388.000000 547.000000 540.000000
max 1051.000000 4.010000 6.854900 780.000000 0.842200 32712.000000 11475.000000 510.000000 751.000000 1295.000000 1155.000000
In [41]:
houe_price_2017.describe(include=[np.number])
Out[41]:
Average value January 2017 Average value February 2017 Average value March 2017 Average value April 2017 Average value May 2017 Average value June 2017 Average value July 2017 Average value August 2017 Average value September 2017 Average value October 2017 Average value November 2017 Average value December 2017
count 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02 1.050000e+02
mean 5.238906e+05 5.263262e+05 5.297206e+05 5.323468e+05 5.356371e+05 5.384297e+05 5.389927e+05 5.401560e+05 5.410792e+05 5.430055e+05 5.467685e+05 5.502356e+05
std 3.028870e+05 3.012903e+05 3.014839e+05 3.007790e+05 3.002828e+05 3.007521e+05 3.010715e+05 3.001208e+05 2.974859e+05 2.976761e+05 3.013470e+05 3.028477e+05
min 1.569710e+05 1.580070e+05 1.622740e+05 1.661850e+05 1.622610e+05 1.571760e+05 1.597550e+05 1.558960e+05 1.660240e+05 1.646540e+05 1.498300e+05 1.559590e+05
25% 3.201800e+05 3.268750e+05 3.348350e+05 3.297700e+05 3.343170e+05 3.457290e+05 3.369320e+05 3.244800e+05 3.438360e+05 3.458790e+05 3.456150e+05 3.476980e+05
50% 4.348540e+05 4.378320e+05 4.495200e+05 4.500430e+05 4.504820e+05 4.534960e+05 4.557250e+05 4.593930e+05 4.674820e+05 4.698420e+05 4.704200e+05 4.708960e+05
75% 6.727520e+05 6.739230e+05 6.763810e+05 6.815900e+05 6.830120e+05 6.798030e+05 6.821180e+05 6.827090e+05 6.867590e+05 6.872410e+05 6.874440e+05 6.937250e+05
max 1.532815e+06 1.540731e+06 1.542858e+06 1.523289e+06 1.524074e+06 1.534921e+06 1.546047e+06 1.542465e+06 1.532259e+06 1.534549e+06 1.570354e+06 1.575133e+06

我观察rent的dataframe中出现了两个min值为零的数一个为nCurr,该值为在周期末活跃磅金的的总数,该值有可能能为零即为未有房屋出租。同时lsd为样本磅金标准差的对数,出现了0的状况,这意味样本标准差出现了极小现象也就是sd出现了missing value

In [42]:
#填补rent表格中标准差的mission
#std = sqrt(mean()abs(x-x.mean())**2)
#np.std(dataframe,ddpf=1)
rent = rent.fillna(np.std(rent,ddof=1))
#only get float 2
rent = rent.round(2)

Data Preprocessing

In [43]:
rent['Location'].value_counts()
Out[43]:
Auckland Region             26
Otago Region                26
Wellington Region           26
NA                          26
Canterbury Region           25
Waikato Region              25
Bay of Plenty Region        22
Manawatu-Wanganui Region    22
Taranaki Region             20
Northland Region            20
Hawke's Bay Region          19
Nelson Region               17
Southland Region            17
Tasman Region               14
Gisborne Region             14
Marlborough Region          13
West Coast Region            9
Name: Location, dtype: int64
In [44]:
houe_price_2017['Location'].value_counts()
Out[44]:
South Waikato District             1
Stratford District                 1
Hamilton - Central & North West    1
Kaikoura District                  1
Auckland City - Central            1
Horowhenua District                1
Auckland_City - East               1
Waitakere City                     1
Manukau - Central                  1
Queenstown-Lakes District          1
Manukau - East                     1
Gisborne District                  1
Lower Hutt City                    1
Central Hawkes Bay District        1
Opotiki District                   1
Waitomo District                   1
Timaru District                    1
Carterton District                 1
Masterton District                 1
Gore District                      1
Tasman District                    1
Otorohanga District                1
Dunedin - Taieri                   1
Rodney District                    1
Wellington - East                  1
Hurunui District                   1
North Shore - North Harbour        1
Dunedin City                       1
Christchurch - Hills               1
MacKenzie District                 1
                                  ..
Waimate District                   1
Rangitikei District                1
Auckland City                      1
Palmerston North City              1
Selwyn District                    1
Taupo District                     1
North Shore City                   1
Hastings District                  1
Hamilton - South East              1
North Shore - Onewa                1
Christchurch - Southwest           1
Franklin District                  1
Porirua City                       1
Hauraki District                   1
Central Otago District             1
Whangarei District                 1
Christchurch City                  1
Ruapehu District                   1
Manawatu District                  1
Waimakariri District               1
Wellington - North                 1
Papakura District                  1
Main Urban Areas                   1
Rotorua District                   1
Matamata-Piako District            1
South Taranaki District            1
Thames-Coromandel District         1
Kapiti Coast District              1
Kaipara District                   1
Dunedin - Central & North          1
Name: Location, Length: 105, dtype: int64
In [45]:
#Matching location from rent to house price 2017
#NorthLand Region
NorthLand = houe_price_2017.loc[[4,5,6,],:]
#Auckland Region
Auckland = houe_price_2017.loc[[7,10,14,15,20,24],:] 
#Waikato Region
Waikato = houe_price_2017.loc[[25,26,27,28,29,30,35,36,37,38,39],:]
#Bay of Plenty Region
Bay_of_Plenty = houe_price_2017.loc[[40,41,42,43,44,45],:]
#Gisborne Region
Gisborne = houe_price_2017.loc[[46],:]
#Hawke's Bay Region
Hawkes_Bay = houe_price_2017.loc[[47,48,49,50],:]
#Taranaki Region
Taranaki = houe_price_2017.loc[[51,52,53],:]
#Manawatu-Wanganui Region
Manawatu_Wanganui = houe_price_2017.loc[[54,55,56,57,58,59,60],:]
#Willington Region
Willionton = houe_price_2017.loc[[61,62,63,64,65,70,71,72],:]
#Marlborough Region
Marlborough = houe_price_2017.loc[[75],:]
#West Coast Region
West_Coast = houe_price_2017.loc[[73,77,78,79],:]
#Tasman Region
Tasman = houe_price_2017.loc[[73],:]
#Canterbury Region
Canterbury = houe_price_2017.loc[[76,80,81,82,88,89,90,91,92],:]
#Otago Region
Otago = houe_price_2017.loc[[93,94,95,96,101],:]
#Nelson Region
Nelson = houe_price_2017.loc[[74],:]
#Southland Region
Southland = houe_price_2017.loc[[102,103,104],:]
In [46]:
#Merge all district which is belong to NorthLand Region
match_data_northland = {'Far North District':'Northland Region','Whangarei District':'Northland Region','Kaipara District':'Northland Region'}
NorthLand['Location'].replace(match_data_northland, inplace=True)
#Merge all district which is belong to Auckland Region
match_data_akl = {'Rodney District':'Auckland Region','North Shore City':'Auckland Region','Waitakere City':'Auckland Region','':'Auckland Region','Manukau City':'Auckland Region','Papakura District':'Auckland Region'}
Auckland['Location'].replace(match_data_akl, inplace=True)
#Merge all district which is belong to Auckland Region
match_data_Waikato = {'Franklin District':'Waikato Region','Thames-Coromandel District':'Waikato Region','Hauraki District':'Waikato Region','Waikato District':'Waikato Region','Matamata-Piako District':'Waikato Region','Hamilton City':'Waikato Region','Waipa District':'Waikato Region','Otorohanga District':'Waikato Region','South Waikato District':'Waikato Region','Waitomo District':'Waikato Region','Taupo District':'Waikato Region'}
Waikato['Location'].replace(match_data_Waikato, inplace=True)
#Merge all district which is belong to Bay of Plenty Region
match_data_bay = {'Western Bay of Plenty District':'Bay of Plenty Region','Tauranga City':'Bay of Plenty Region','Rotorua District':'Bay of Plenty Region','Whakatane District':'Bay of Plenty Region','Kawerau District':'Bay of Plenty Region','Opotiki District':'Bay of Plenty Region'}
Bay_of_Plenty['Location'].replace(match_data_bay, inplace=True)
#Merge all district which is belong to Auckland Region
match_data_hawkes = {'Wairoa District':'Hawkes Bay Region','Hastings District':"Hawkes Bay Region",'Napier City':"Hawkes Bay Region",'Central Hawkes Bay District':"Hawkes Bay Region"}
Hawkes_Bay['Location'].replace(match_data_hawkes, inplace=True)
#Merge all district which is belong to Taranaki Region
match_data_Taranaki = {'New Plymouth District':'Taranaki Region','Stratford District':'Taranaki Region','South Taranaki District':'Taranaki Region'}
Taranaki['Location'].replace(match_data_Taranaki, inplace=True)
#Merge all district which is belong to Manawatu-Wanganui Region 
match_data_Manawatu_Wanganui = {'Ruapehu District':'Manawatu-Wanganui Region','Whanganui District':'Manawatu-Wanganui Region', 'Manawatu District':'Manawatu-Wanganui Region','Rangitikei District':'Manawatu-Wanganui Region', 'Palmerston North City':'Manawatu-Wanganui Region', 'Tararua District':'Manawatu-Wanganui Region','Horowhenua District':'Manawatu-Wanganui Region'}
Manawatu_Wanganui['Location'].replace(match_data_Manawatu_Wanganui, inplace=True)
#Merge all district which is belong to Willionton Region 
match_data_Willionton = {'Kapiti Coast District':'Wellington Region','Porirua City':'Wellington Region','Upper Hutt City':'Wellington Region','Lower Hutt City':'Wellington Region','Wellington City':'Wellington Region','Masterton District':'Wellington Region', 'Carterton District':'Wellington Region','South Wairarapa District':'Wellington Region'}
Willionton['Location'].replace(match_data_Willionton, inplace=True)
#Merge all district which is belong to West Coast Region 
match_data_West_Coast = {'Tasman District':'West Coast Region', 'Buller District':'West Coast Region', 'Grey District':'West Coast Region','Westland District':'West Coast Region'}
West_Coast['Location'].replace(match_data_West_Coast, inplace=True)
#Merge all district which is belong to Canterbury Region 
match_data_Canterbury = {'Kaikoura District':'Canterbury Region','Hurunui District':'Canterbury Region','Waimakariri District':'Canterbury Region', 'Christchurch City':'Canterbury Region','Selwyn District':'Canterbury Region','Ashburton District':'Canterbury Region', 'Timaru District':'Canterbury Region','MacKenzie District':'Canterbury Region','MacKenzie District':'Canterbury Region'}
Canterbury['Location'].replace(match_data_Canterbury, inplace=True)
#Merge all district which is belong to Otago Region 
match_data_Otago = {'Waitaki District':'Otago Region','Central Otago District':'Otago Region','Queenstown-Lakes District':'Otago Region', 'Dunedin City':'Otago Region','Clutha District':'Otago Region'}
Otago['Location'].replace(match_data_Otago, inplace=True)
#Merge all district which is belong to Southland Region
match_data_Southland = {'Southland District':'Southland Region','Gore District':'Southland Region','Invercargill City':'Southland Region'}
Southland['Location'].replace(match_data_Southland, inplace=True)
#Merge all district which is belong to Gisborne Region
match_data_Gisborne = {'Gisborne District':'Gisborne'}
Gisborne['Location'].replace(match_data_Gisborne, inplace=True)
#Merge all district which is belong to Marlborough Region
match_data_Marlborough = {'Marlborough District':'Marlborough Region'}
Marlborough['Location'].replace(match_data_Marlborough, inplace=True)
#Merge all district which is belong to Tasman Region
match_data_Tasman = {'Tasman District':'Tasman Region'}
Tasman['Location'].replace(match_data_Tasman, inplace=True)
#Merge all district which is belong to Nelson Region
match_data_Nelson = {'Nelson City':'Nelson Region'}
Nelson['Location'].replace(match_data_Nelson, inplace=True)
In [47]:
#Concat all the location in housr price dataframe
location = [NorthLand, Auckland, Waikato, Bay_of_Plenty, Gisborne, Hawkes_Bay, Taranaki, Manawatu_Wanganui, Willionton, Marlborough, West_Coast, Tasman, Canterbury, Otago, Nelson, Southland]
house_price_2017_new = pd.concat(location)
#Add totoal number of different area
sum_house = house_price_2017_new.sum(1)
house_price_2017_new['Total'] = sum_house
In [48]:
#Merge rent and house price to one dataframe
house_rent = pd.merge(rent,house_price_2017_new, on='Location')
house_rent.head()
Out[48]:
Houeing Type Location Mean of rent Number of Bedrooms brr lmean lq lsd nCurr nLodged ... Average value April 2017 Average value May 2017 Average value June 2017 Average value July 2017 Average value August 2017 Average value September 2017 Average value October 2017 Average value November 2017 Average value December 2017 Total
0 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 956733 960087 955814 949658 945934 939955 933909 935590 941029 11329743
1 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 1195292 1198381 1203775 1202461 1200914 1195052 1201452 1212617 1226509 14449098
2 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 826454 825736 823630 819426 816503 816408 818706 821105 824271 9889477
3 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 903160 902068 900766 898361 899432 897957 893580 891394 895606 10786547
4 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 693239 683812 677340 675358 666387 679072 684268 692175 696713 8208860

5 rows × 27 columns

In [49]:
#Check missing values
house_rent.isnull().sum()
Out[49]:
Houeing Type                    0
Location                        0
Mean of rent                    0
Number of Bedrooms              0
brr                             0
lmean                           0
lq                              0
lsd                             0
nCurr                           0
nLodged                         0
sd                              0
slq                             0
suq                             0
uq                              0
Average value January 2017      0
Average value February 2017     0
Average value March 2017        0
Average value April 2017        0
Average value May 2017          0
Average value June 2017         0
Average value July 2017         0
Average value August 2017       0
Average value September 2017    0
Average value October 2017      0
Average value November 2017     0
Average value December 2017     0
Total                           0
dtype: int64

Convert categorical variables to dummies

In [50]:
dummy = pd.get_dummies(house_rent[['Houeing Type', 'Location', 'Number of Bedrooms']])
house_rent_text = pd.concat([house_rent, dummy], axis=1) 
house_rent_text
Out[50]:
Houeing Type Location Mean of rent Number of Bedrooms brr lmean lq lsd nCurr nLodged ... Location_Tasman Region Location_Waikato Region Location_Wellington Region Location_West Coast Region Number of Bedrooms_1 Number of Bedrooms_2 Number of Bedrooms_3 Number of Bedrooms_4 Number of Bedrooms_5+ Number of Bedrooms_NA
0 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
1 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
2 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
3 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
4 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
5 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
6 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
7 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
8 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
9 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
10 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
11 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
12 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
13 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
14 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
15 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
16 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
17 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
18 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
19 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
20 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
21 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
22 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
23 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
24 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
25 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
26 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
27 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
28 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
29 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1450 Flat West Coast Region 206 2 3.23 5.30 175 0.22 129 63 ... 0 0 0 1 0 1 0 0 0 0
1451 Flat West Coast Region 206 2 3.23 5.30 175 0.22 129 63 ... 0 0 0 1 0 1 0 0 0 0
1452 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1453 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1454 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1455 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1456 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1457 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1458 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1459 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1460 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1461 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1462 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1463 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1464 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1465 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1466 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1467 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1468 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1469 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1470 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1471 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1472 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1473 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1474 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1475 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1476 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0
1477 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0
1478 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0
1479 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0

1480 rows × 52 columns

Data Visualization

House type analysis

Through the histogram analysis of the house type, we can see that House and Flat are more popular among renters, and the number of people renting a room is the least.

In [118]:
rent.groupby('Houeing Type')['Houeing Type'].count()
sns.factorplot('Houeing Type', data=rent, kind='count', aspect=3)
plt.title('Histogram of House type analysis')
Out[118]:
Text(0.5,1,'Histogram of House type analysis')

House type and bedroom number analysis

There are we can see that the renting method of a room has little difference in the type of room, but in the data of the five rooms, the least of House is the Apartment, which I think may also be the reason for the construction of the house. At the same time, Room and Apartment are the least in the unknown bedroom data.

In [119]:
rent.groupby(['Houeing Type', 'Number of Bedrooms'])['Houeing Type'].count()
g = sns.factorplot('Number of Bedrooms', data=rent, hue='Houeing Type', kind='count', aspect=1.75)
g.set_xlabels('Different House bedroom')
plt.title('Relation of House type and Bedroom number')
Out[119]:
Text(0.5,1,'Relation of House type and Bedroom number')

Location number and Distribution

In the map of Location number and Distribution, we can see that the distribution of housing rentals in most areas is relatively average, except for the West Coast Region. At the same time, the density of rental housing is mainly concentrated between 20-26. This includes Auckland Region, Otago Region, Wellington Region, NA, Canterbury Region, Waikato Region, Bay of Plenty Region, Manawatu-Wanganui Region, Taranaki Region, Northland Region these areas.

In [53]:
area = rent['Location'].value_counts()
Area_dist = sns.distplot(area)
Area_dist.set_title("Distribution of Rent Location")
area
E:\Anaconda\lib\site-packages\matplotlib\axes\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "
Out[53]:
Auckland Region             26
Otago Region                26
Wellington Region           26
NA                          26
Canterbury Region           25
Waikato Region              25
Bay of Plenty Region        22
Manawatu-Wanganui Region    22
Taranaki Region             20
Northland Region            20
Hawke's Bay Region          19
Nelson Region               17
Southland Region            17
Tasman Region               14
Gisborne Region             14
Marlborough Region          13
West Coast Region            9
Name: Location, dtype: int64

Mean of rent price

The rent range is mainly concentrated in the approximate range of 210 to 560, and there are also a very small number of high prices. At the same time, we can see that the most frequent rent is around 400.

In [117]:
house_rent['Mean of rent'].hist(bins=50)
plt.xlabel('Mean of rent')
plt.title('Histogram of Mean rent')
Out[117]:
Text(0.5,1,'Histogram of Mean rent')

Boxplot of location and bonds price

In the Boxplot chart we can see that the high house prices appear in the Auckland area, followed by the unknown area of NA, and the Wellington area. The lowest rent distribution for the entire New Zealand region is between 180 and 210. The gap between the 95% confidence intervals is quite obvious.

In [120]:
rent[['Mean of rent', 'Location']].boxplot(column='Mean of rent', by='Location', figsize=(20,8))
plt.xticks(rotation=45)
Out[120]:
(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17]),
 <a list of 17 Text xticklabel objects>)

Get mean of rent and number of bonds lodged in 2017 relation by bedrooms type

The relationship between the Bonds submitted in 2017 and the average rent is summarized by the different rental Bedroom types. As can be seen from the figure, the Bounds Lodged with the largest difference in rents for 5+ houses is the least active, while the rent range for one bedroom and two bedrooms is dense. The three-bedroom Bounds Lodged is very active and dominates the house type of House.

In [104]:
#Relation between number of bonds lodged at some point in the 2017, bedrooms type and mean of rent
#ax.tick_params(labelsize=8)
g = sns.FacetGrid(house_rent, col="Number of Bedrooms", hue='Houeing Type', aspect=4, col_wrap=2)
g.map(plt.scatter, "Mean of rent", "nLodged", alpha=.7)
g.add_legend();

Get different location house price in 2017

The table for Boxplot by Location and House Total price is very interesting. The difference between the average value and the lowest value is very large. This proves that the price difference between the different Regions of New Zealand is very uneven, with the Auckland Region as the highest confidence interval. The price of Manawatu-Wanganui Region is the minimum. In a small number of areas, there is only an average due to incomplete data.

In [65]:
house_rent[['Total', 'Location']].boxplot(column='Total', by='Location', figsize=(28,10))
plt.xticks(rotation=45)
Out[65]:
(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),
 <a list of 14 Text xticklabel objects>)

Predictive Modeling

Linear Regression

In [105]:
import statsmodels.formula.api as smf
import statsmodels.api as sm
from statsmodels.sandbox.regression.predstd import wls_prediction_std
In [72]:
house_rent_text.pivot_table(values={ 'rent_m', 
                                    'Number of Bedrooms', 
                                    'brr', 
                                    'lmean', 
                                    'lq', 
                                    'lsd', 
                                    'nCurr', 
                                    'nLodged', 
                                    'sd',
                                    'slq',
                                    'suq',
                                    'uq',
                                    'Average value January 2017',
                                     'Average value February 2017',
                                     'Average value March 2017',
                                     'Average value April 2017',
                                     'Average value May 2017',
                                     'Average value June 2017',
                                     'Average value July 2017',
                                     'Average value August 2017',
                                     'Average value September 2017',
                                     'Average value October 2017',
                                     'Average value November 2017',
                                     'Average value December 2017',
                                     'Total'
                                   }, index=['Location'], aggfunc=[ np.median, np.max, np.min])
Out[72]:
median ... amin
Average value April 2017 Average value August 2017 Average value December 2017 Average value February 2017 Average value January 2017 Average value July 2017 Average value June 2017 Average value March 2017 Average value May 2017 Average value November 2017 ... lmean lq lsd nCurr nLodged rent_m sd slq suq uq
Location
Auckland Region 903160.0 899432.0 895606.0 902477.0 901422.0 898361.0 900766.0 900324.0 902068.0 891394.0 ... 5.42 190 0.17 24 12 237 74.0 189 273 265
Bay of Plenty Region 394376.5 405430.0 413699.5 388836.0 380278.0 401411.0 396417.5 395231.0 395795.5 412688.5 ... 5.14 170 0.06 6 9 171 11.0 164 178 170
Canterbury Region 420799.5 414353.0 426943.0 411936.5 409486.5 416128.0 411678.0 422523.5 428878.0 415960.0 ... 5.29 145 0.14 15 12 206 52.0 131 238 208
Manawatu-Wanganui Region 220336.0 229961.0 235900.0 212695.0 207752.0 229167.0 226526.0 217672.0 222406.0 233750.0 ... 5.12 145 0.15 15 6 174 28.0 140 200 180
Marlborough Region 428509.0 440276.0 450525.0 425213.0 423753.0 438292.0 437984.0 427552.0 434357.0 447194.0 ... 5.35 186 0.02 6 6 230 9.0 169 265 200
Nelson Region 527422.0 538136.0 555184.0 513933.0 508343.0 531659.0 532120.0 522201.0 527974.0 553052.0 ... 5.26 210 0.10 9 6 196 27.0 170 219 210
Northland Region 477765.0 497489.0 496551.0 468033.0 463319.0 494212.0 492588.0 471203.0 483049.0 492074.0 ... 5.20 140 0.10 9 6 189 22.0 149 223 223
Otago Region 371739.0 375814.0 391098.0 359629.0 359055.0 373857.0 375371.0 363821.0 373810.0 386326.0 ... 5.34 165 0.22 15 6 228 63.0 160 270 239
Southland Region 239486.0 240725.0 256433.0 235895.0 236549.0 242829.0 241770.0 237168.0 243144.0 251884.0 ... 4.61 95 0.09 3 6 103 20.0 87 116 100
Taranaki Region 233938.0 244076.0 252131.0 232541.0 234372.0 235850.0 236211.0 234368.0 235861.0 256933.0 ... 5.19 135 0.11 9 6 192 22.0 136 223 219
Tasman Region 521575.0 538256.0 556009.0 501153.0 498111.0 533816.0 535118.0 512754.0 534908.0 553187.0 ... 4.90 90 0.06 0 6 143 18.0 105 173 175
Waikato Region 437943.0 441138.0 459226.0 428410.0 418130.0 445296.0 446364.0 435052.0 440842.0 458530.0 ... 5.31 175 0.08 12 6 209 43.0 164 247 237
Wellington Region 468417.5 484083.5 497024.0 458827.5 458085.0 480703.5 478300.5 462584.0 475769.5 497260.0 ... 5.30 170 0.23 18 9 207 53.0 167 238 235
West Coast Region 224577.0 225826.0 225365.0 219878.0 223092.5 227870.0 231985.5 223176.5 230764.0 225499.0 ... 4.50 90 0.00 6 9 90 30.0 90 90 90

14 rows × 72 columns

In [69]:
#Analysis correlation coefficient
house_rent_text.corr()
Out[69]:
rent_m brr lmean lq lsd nCurr nLodged sd slq suq ... Location_Tasman Region Location_Waikato Region Location_Wellington Region Location_West Coast Region Number of Bedrooms_1 Number of Bedrooms_2 Number of Bedrooms_3 Number of Bedrooms_4 Nb5 Number of Bedrooms_NA
rent_m 1.000000 -0.100301 0.959333 0.945897 0.225500 0.116403 0.111990 0.715970 0.948398 0.992660 ... -0.036749 -0.069868 0.295665 -0.177320 -0.477489 -0.194939 0.033590 0.288325 0.587908 -0.115648
brr -0.100301 1.000000 -0.033657 -0.061507 -0.193157 0.030994 0.011867 -0.236626 -0.048296 -0.111073 ... -0.050100 0.251467 -0.292138 -0.153331 -0.036220 0.076664 0.113290 0.034754 -0.046569 -0.141998
lmean 0.959333 -0.033657 1.000000 0.943580 0.114728 0.156581 0.151593 0.590506 0.953937 0.936559 ... -0.029739 -0.026752 0.248257 -0.227511 -0.560896 -0.147953 0.104724 0.330719 0.500047 -0.107119
lq 0.945897 -0.061507 0.943580 1.000000 -0.027683 0.136134 0.133334 0.505536 0.981189 0.910478 ... -0.025398 0.002442 0.187985 -0.168242 -0.474748 -0.182563 0.064979 0.358749 0.533748 -0.181654
lsd 0.225500 -0.193157 0.114728 -0.027683 1.000000 -0.096716 -0.106156 0.747697 -0.073658 0.330112 ... -0.052343 -0.142021 0.326253 -0.087689 -0.036586 -0.146233 -0.139549 -0.144804 0.214181 0.273503
nCurr 0.116403 0.030994 0.156581 0.136134 -0.096716 1.000000 0.985303 0.015921 0.153550 0.099190 ... -0.036671 -0.009703 0.010761 -0.058827 -0.103571 0.090166 0.225075 -0.020470 -0.113420 -0.083016
nLodged 0.111990 0.011867 0.151593 0.133334 -0.106156 0.985303 1.000000 0.010151 0.151635 0.093823 ... -0.039670 -0.017722 0.006794 -0.062187 -0.072004 0.097701 0.217955 -0.019754 -0.112665 -0.118603
sd 0.715970 -0.236626 0.590506 0.505536 0.747697 0.015921 0.010151 1.000000 0.481873 0.779060 ... -0.060648 -0.177639 0.370264 -0.113458 -0.268300 -0.184994 -0.080812 0.025024 0.449502 0.135008
slq 0.948398 -0.048296 0.953937 0.981189 -0.073658 0.153550 0.151635 0.481873 1.000000 0.904170 ... -0.023521 -0.016738 0.184322 -0.172981 -0.486664 -0.165097 0.074835 0.352013 0.522840 -0.180104
suq 0.992660 -0.111073 0.936559 0.910478 0.330112 0.099190 0.093823 0.779060 0.904170 1.000000 ... -0.040563 -0.089350 0.331742 -0.173695 -0.460601 -0.201297 0.018451 0.258350 0.597074 -0.091908
uq 0.985229 -0.084929 0.935336 0.895951 0.308440 0.098613 0.092923 0.744203 0.902331 0.991057 ... -0.037006 -0.078710 0.340231 -0.171302 -0.466679 -0.194357 0.026249 0.254431 0.606795 -0.104782
Average value January 2017 0.333910 -0.063054 0.325805 0.317002 0.039117 0.251277 0.264208 0.262407 0.334642 0.321995 ... 0.028080 -0.043048 0.018602 -0.107366 -0.017200 -0.021077 -0.016001 0.008379 0.031335 0.019888
Average value February 2017 0.335414 -0.062165 0.327730 0.318223 0.041367 0.249700 0.262386 0.263853 0.335513 0.323719 ... 0.027967 -0.036403 0.024779 -0.112157 -0.017396 -0.021376 -0.016178 0.008568 0.032075 0.019745
Average value March 2017 0.334924 -0.063517 0.327049 0.317294 0.042422 0.248252 0.260846 0.263887 0.334549 0.323454 ... 0.031256 -0.039169 0.029163 -0.110333 -0.017256 -0.021326 -0.016157 0.008536 0.031934 0.019679
Average value April 2017 0.333798 -0.061837 0.325893 0.316197 0.042002 0.247090 0.259451 0.263011 0.333373 0.322407 ... 0.033970 -0.041123 0.029901 -0.109261 -0.017139 -0.021106 -0.015933 0.008456 0.031491 0.019575
Average value May 2017 0.331734 -0.062877 0.324476 0.314725 0.041383 0.244504 0.256854 0.260580 0.331512 0.320315 ... 0.037695 -0.033763 0.029825 -0.108466 -0.017298 -0.021137 -0.016064 0.008560 0.031535 0.019770
Average value June 2017 0.332669 -0.061116 0.325481 0.315164 0.044496 0.242079 0.253947 0.262674 0.331473 0.321624 ... 0.036638 -0.026870 0.035165 -0.109972 -0.017426 -0.021252 -0.016055 0.008671 0.032127 0.019389
Average value July 2017 0.332715 -0.064192 0.325603 0.314453 0.047720 0.239871 0.251754 0.264265 0.330591 0.322041 ... 0.035593 -0.030444 0.039019 -0.112834 -0.017470 -0.021373 -0.016158 0.008746 0.032294 0.019441
Average value August 2017 0.333357 -0.064587 0.326293 0.314636 0.049803 0.238235 0.249890 0.265586 0.330564 0.322940 ... 0.036944 -0.028449 0.043829 -0.113760 -0.017422 -0.021432 -0.016123 0.008809 0.032421 0.019243
Average value September 2017 0.335182 -0.065343 0.327991 0.316252 0.050680 0.239006 0.250684 0.267275 0.332135 0.324800 ... 0.037275 -0.025693 0.047108 -0.115903 -0.017479 -0.021662 -0.016275 0.008747 0.032780 0.019429
Average value October 2017 0.334576 -0.067034 0.327112 0.315147 0.052206 0.237438 0.249023 0.267577 0.330903 0.324470 ... 0.039007 -0.025877 0.053066 -0.116157 -0.017393 -0.021661 -0.016307 0.008607 0.032972 0.019333
Average value November 2017 0.336573 -0.071021 0.328168 0.315687 0.055292 0.237844 0.249507 0.271291 0.331754 0.326870 ... 0.040918 -0.039615 0.060021 -0.116552 -0.017309 -0.021466 -0.016316 0.008438 0.032789 0.019378
Average value December 2017 0.337567 -0.072862 0.329237 0.316603 0.055580 0.238594 0.250466 0.272011 0.332753 0.327820 ... 0.040495 -0.040939 0.060114 -0.117707 -0.017445 -0.021619 -0.016491 0.008489 0.032999 0.019616
Total 0.334641 -0.065020 0.327003 0.316207 0.046873 0.243027 0.255132 0.265583 0.332753 0.323801 ... 0.035515 -0.034322 0.039238 -0.112627 -0.017367 -0.021391 -0.016185 0.008591 0.032255 0.019557
Houeing Type_Apartment 0.147729 -0.139144 0.160477 0.130651 -0.039830 -0.123924 -0.089969 0.094603 0.161069 0.131635 ... -0.013813 0.048641 0.031830 -0.078781 -0.000803 0.051010 0.032361 0.003536 -0.040274 -0.050125
Houeing Type_Flat -0.089692 -0.123886 -0.099974 -0.064286 -0.008425 -0.056372 -0.075801 -0.079507 -0.089286 -0.083849 ... 0.009348 -0.005106 -0.013006 0.032596 -0.035242 0.016146 0.020353 0.019529 -0.005941 -0.011871
Houeing Type_House 0.076834 0.083157 0.112339 0.076973 -0.020992 0.362057 0.350667 0.009671 0.089771 0.073240 ... 0.037156 -0.023358 -0.028266 0.065738 -0.069305 -0.001703 0.002579 0.028343 0.063345 -0.008938
Houeing Type_NA 0.046859 0.340197 0.069813 0.043088 0.056448 -0.161850 -0.178226 0.053169 0.030397 0.053085 ... -0.037724 0.005480 -0.004163 -0.046177 -0.063604 0.001165 0.009424 0.007182 0.031314 0.023429
Houeing Type_Room -0.313341 -0.304360 -0.423324 -0.324164 0.020380 -0.070736 -0.042251 -0.128616 -0.331133 -0.301260 ... 0.004946 -0.039310 0.029862 0.034487 0.308262 -0.114444 -0.113355 -0.107019 -0.095033 0.080848
Location_Auckland Region 0.328117 0.014631 0.296911 0.315545 -0.017949 0.342258 0.356542 0.257438 0.343984 0.310239 ... -0.030325 -0.148244 -0.125485 -0.048997 -0.011575 -0.014629 -0.012484 0.000403 0.027067 0.015285
Location_Bay of Plenty Region -0.022704 0.249107 0.005220 -0.019052 -0.015797 -0.042701 -0.056318 -0.066931 -0.012230 -0.023096 ... -0.030580 -0.149491 -0.126541 -0.049409 0.015312 0.008462 0.010773 0.024697 -0.032257 -0.030801
Location_Canterbury Region -0.016744 -0.185014 0.015672 0.005737 -0.075163 0.041124 0.068368 -0.081146 0.016675 -0.030412 ... -0.038628 -0.188835 -0.159845 -0.062413 -0.007246 -0.012184 -0.009409 0.007264 -0.005976 0.027498
Location_Manawatu-Wanganui Region -0.213118 0.130850 -0.222596 -0.205286 -0.048323 -0.072358 -0.080820 -0.155972 -0.204808 -0.208567 ... -0.033303 -0.162803 -0.137809 -0.053809 0.016676 0.009216 0.011733 -0.016091 0.011710 -0.033543
Location_Marlborough Region -0.032586 0.013158 -0.020689 -0.018491 -0.084761 -0.035908 -0.039144 -0.054763 -0.013469 -0.040495 ... -0.009199 -0.044971 -0.038067 -0.014864 -0.012441 0.033972 0.015540 0.000122 -0.013685 -0.024037
Location_Nelson Region -0.018855 0.038041 -0.001303 0.002904 -0.096127 -0.037897 -0.040882 -0.064891 0.004475 -0.027727 ... -0.010534 -0.051496 -0.043590 -0.017020 0.007407 0.001386 0.019096 -0.010689 -0.021570 0.000803
Location_Northland Region -0.092789 0.121743 -0.071027 -0.064154 -0.100831 -0.059381 -0.066173 -0.114552 -0.070646 -0.098052 ... -0.020088 -0.098198 -0.083123 -0.032456 -0.003768 0.015471 0.017052 -0.001927 -0.046616 0.014299
Location_Otago Region 0.071306 -0.279133 0.066738 0.030300 0.227528 -0.072880 -0.065248 0.208704 0.016663 0.088436 ... -0.030325 -0.148244 -0.125485 -0.048997 -0.011575 -0.014629 -0.012484 0.000403 0.027067 0.015285
Location_Southland Region -0.191654 0.141443 -0.228148 -0.181817 -0.089798 -0.062315 -0.066591 -0.148467 -0.182149 -0.189632 ... -0.018461 -0.090249 -0.076394 -0.029829 0.012981 0.031902 0.003808 -0.018732 -0.037802 0.001407
Location_Taranaki Region -0.112526 0.021647 -0.111898 -0.113674 0.002405 -0.065574 -0.070680 -0.061090 -0.113841 -0.108424 ... -0.020088 -0.098198 -0.083123 -0.032456 0.021580 0.015471 0.017052 -0.001927 -0.046616 -0.012833
Location_Tasman Region -0.036749 -0.050100 -0.029739 -0.025398 -0.052343 -0.036671 -0.039670 -0.060648 -0.023521 -0.040563 ... 1.000000 -0.046684 -0.039517 -0.015430 0.018867 0.011058 -0.006797 -0.002853 -0.015830 -0.007943
Location_Waikato Region -0.069868 0.251467 -0.026752 0.002442 -0.142021 -0.009703 -0.017722 -0.177639 -0.016738 -0.089350 ... -0.046684 1.000000 -0.193179 -0.075429 -0.008757 -0.014724 -0.011372 0.008778 0.050557 -0.017213
Location_Wellington Region 0.295665 -0.292138 0.248257 0.187985 0.326253 0.010761 0.006794 0.370264 0.184322 0.331742 ... -0.039517 -0.193179 1.000000 -0.063849 -0.015084 -0.019064 -0.016267 0.000525 0.035271 0.019919
Location_West Coast Region -0.177320 -0.153331 -0.227511 -0.168242 -0.087689 -0.058827 -0.062187 -0.113458 -0.172981 -0.173695 ... -0.015430 -0.075429 -0.063849 1.000000 0.005759 0.021190 0.022463 -0.018520 -0.059679 0.020246
Number of Bedrooms_1 -0.477489 -0.036220 -0.560896 -0.474748 -0.036586 -0.103571 -0.072004 -0.268300 -0.486664 -0.460601 ... 0.018867 -0.008757 -0.015084 0.005759 1.000000 -0.232858 -0.230642 -0.217750 -0.193362 -0.234517
Number of Bedrooms_2 -0.194939 0.076664 -0.147953 -0.182563 -0.146233 0.090166 0.097701 -0.184994 -0.165097 -0.201297 ... 0.011058 -0.014724 -0.019064 0.021190 -0.232858 1.000000 -0.205206 -0.193735 -0.172037 -0.208653
Number of Bedrooms_3 0.033590 0.113290 0.104724 0.064979 -0.139549 0.225075 0.217955 -0.080812 0.074835 0.018451 ... -0.006797 -0.011372 -0.016267 0.022463 -0.230642 -0.205206 1.000000 -0.191891 -0.170400 -0.206667
Number of Bedrooms_4 0.288325 0.034754 0.330719 0.358749 -0.144804 -0.020470 -0.019754 0.025024 0.352013 0.258350 ... -0.002853 0.008778 0.000525 -0.018520 -0.217750 -0.193735 -0.191891 1.000000 -0.160875 -0.195115
Nb5 0.587908 -0.046569 0.500047 0.533748 0.214181 -0.113420 -0.112665 0.449502 0.522840 0.597074 ... -0.015830 0.050557 0.035271 -0.059679 -0.193362 -0.172037 -0.170400 -0.160875 1.000000 -0.173262
Number of Bedrooms_NA -0.115648 -0.141998 -0.107119 -0.181654 0.273503 -0.083016 -0.118603 0.135008 -0.180104 -0.091908 ... -0.007943 -0.017213 0.019919 0.020246 -0.234517 -0.208653 -0.206667 -0.195115 -0.173262 1.000000

49 rows × 49 columns

In [92]:
#Change unrecognized column names
house_rent_text =  house_rent_text.rename(columns={'Number of Bedrooms_5+': 'Nb5','Mean of rent': 'rent_m','Houeing Type':'hstyp' })

A R-squared value of 0.513 was obtained by using the Sample Standard Deviation of weekly rent

In [71]:
#generate the x-axis values that are in range for the Sample Standard Deviation of weekly rent values
x = pd.DataFrame({'sd': np.linspace(house_rent_text.sd.min(), house_rent_text.sd.max(), len(house_rent_text.sd))})

#generate the model which uses the Sample Standard Deviation of weekly rent work score to predict the rent mean
mod = smf.ols(formula='rent_m ~ 1 +sd', data=house_rent_text.dropna()).fit()

#plot the actual data
plt.scatter(house_rent_text.sd, house_rent_text.rent_m, s=20, alpha=0.6)
plt.xlabel('Sample Standard Deviation of weekly rent'); plt.ylabel('Mean of rent')

#render the regression line by predicting the ys using the generated model from above
plt.plot(x.sd, mod.predict(x), 'r', label='Linear $R^2$=%.2f' % mod.rsquared, alpha=0.9)

#give the figure a meaningful legend
plt.legend(loc='upper left', framealpha=0.5, prop={'size':'small'})
plt.title("Predicting rent fee results based on Sample Standard Deviation of weekly rent", fontsize=30)
mod.summary()
Out[71]:
OLS Regression Results
Dep. Variable: rent_m R-squared: 0.513
Model: OLS Adj. R-squared: 0.512
Method: Least Squares F-statistic: 1555.
Date: Mon, 03 Sep 2018 Prob (F-statistic): 6.31e-233
Time: 00:03:11 Log-Likelihood: -9102.2
No. Observations: 1480 AIC: 1.821e+04
Df Residuals: 1478 BIC: 1.822e+04
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
Intercept 231.2334 5.393 42.873 0.000 220.654 241.813
sd 1.5676 0.040 39.427 0.000 1.490 1.646
Omnibus: 42.627 Durbin-Watson: 0.209
Prob(Omnibus): 0.000 Jarque-Bera (JB): 59.933
Skew: 0.297 Prob(JB): 9.68e-14
Kurtosis: 3.787 Cond. No. 248.


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Try to find a 95% confidence interval

In [107]:
#generate the model
mod = smf.ols(formula='rent_m ~ 1 +sd', data=house_rent_text.dropna()).fit()

#extract the parameters for the confidence window
x_pred = np.linspace(house_rent_text.sd.min(), house_rent_text.sd.max(), len(house_rent_text.sd))
x_pred2 = sm.add_constant(x_pred)

#confidence = 95% (alpha=0.05)
sdev, lower, upper = wls_prediction_std(mod, exog=x_pred2, alpha=0.05)

#plot points and confidence window
plt.scatter(house_rent_text.sd, house_rent_text.rent_m, s=10, alpha=0.9)
plt.fill_between(x_pred, lower, upper, color='#888888', alpha=0.2)

#plot the regression line
plt.plot(house_rent_text.sd.dropna(), mod.predict(house_rent_text[['sd']] ), 'b-', label='Linear n=1 $R^2$=%.2f' % mod.rsquared, alpha=0.9)

plt.xlabel('Sample Standard Deviation of weekly rent')
plt.ylabel('Mean Rent')
Out[107]:
Text(0,0.5,'Mean Rent')

Regression analysis was performed by the dummy value of five rooms, and a value of 0.346 was obtained for R-squared.

In [73]:
#generate the x-axis values that are in range for the 5+ Bedroom values
x = pd.DataFrame({'Nb5': np.linspace(house_rent_text.Nb5.min(), house_rent_text.Nb5.max(), len(house_rent_text.Nb5))})

#generate the model which uses the Sample Standard Deviation of weekly rent work score to predict the rent mean
mod = smf.ols(formula='rent_m ~ 1 +Nb5', data=house_rent_text.dropna()).fit()

#plot the actual data
plt.scatter(house_rent_text.Nb5, house_rent_text.rent_m, s=20, alpha=0.6)
plt.xlabel('5+ Bedroom'); plt.ylabel('Mean of rent')

#render the regression line by predicting the ys using the generated model from above
plt.plot(x.Nb5, mod.predict(x), 'r', label='Linear $R^2$=%.2f' % mod.rsquared, alpha=0.9)

#give the figure a meaningful legend
plt.legend(loc='upper left', framealpha=0.5, prop={'size':'small'})
plt.title("Predicting rent fee results based on Bedroom number", fontsize=30)
mod.summary()
Out[73]:
OLS Regression Results
Dep. Variable: rent_m R-squared: 0.346
Model: OLS Adj. R-squared: 0.345
Method: Least Squares F-statistic: 780.7
Date: Mon, 03 Sep 2018 Prob (F-statistic): 2.74e-138
Time: 00:04:03 Log-Likelihood: -9320.2
No. Observations: 1480 AIC: 1.864e+04
Df Residuals: 1478 BIC: 1.865e+04
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
Intercept 373.1390 3.655 102.097 0.000 365.970 380.308
Nb5 288.8286 10.337 27.941 0.000 268.551 309.106
Omnibus: 104.538 Durbin-Watson: 0.161
Prob(Omnibus): 0.000 Jarque-Bera (JB): 125.517
Skew: 0.695 Prob(JB): 5.55e-28
Kurtosis: 3.325 Cond. No. 3.08


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Try to find a 95% confidence interval by 5+ valueBedroom

In [109]:
#generate the model
mod = smf.ols(formula='rent_m ~ 1 +Nb5', data=house_rent_text.dropna()).fit()

#extract the parameters for the confidence window
x_pred = np.linspace(house_rent_text.Nb5.min(), house_rent_text.Nb5.max(), len(house_rent_text.Nb5))
x_pred2 = sm.add_constant(x_pred)

#confidence = 95% (alpha=0.05)
sdev, lower, upper = wls_prediction_std(mod, exog=x_pred2, alpha=0.05)

#plot points and confidence window
plt.scatter(house_rent_text.Nb5, house_rent_text.rent_m, s=10, alpha=0.9)
plt.fill_between(x_pred, lower, upper, color='#888888', alpha=0.2)

#plot the regression line
plt.plot(house_rent_text.Nb5.dropna(), mod.predict(house_rent_text[['Nb5']] ), 'b-', label='Linear n=1 $R^2$=%.2f' % mod.rsquared, alpha=0.9)

plt.xlabel('5+ Bedroom House Type')
plt.ylabel('Mean Rent')
Out[109]:
Text(0,0.5,'Mean Rent')

KNN prediction

In [60]:
from sklearn import neighbors
In [58]:
house_rent_text
Out[58]:
Houeing Type Location Mean of rent Number of Bedrooms brr lmean lq lsd nCurr nLodged ... Location_Tasman Region Location_Waikato Region Location_Wellington Region Location_West Coast Region Number of Bedrooms_1 Number of Bedrooms_2 Number of Bedrooms_3 Number of Bedrooms_4 Number of Bedrooms_5+ Number of Bedrooms_NA
0 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
1 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
2 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
3 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
4 Apartment Auckland Region 430 1 3.51 6.04 370 0.22 5715 3762 ... 0 0 0 0 1 0 0 0 0 0
5 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
6 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
7 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
8 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
9 Apartment Auckland Region 544 2 3.55 6.27 460 0.23 6195 3699 ... 0 0 0 0 0 1 0 0 0 0
10 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
11 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
12 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
13 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
14 Apartment Auckland Region 681 3 3.53 6.47 540 0.33 1494 675 ... 0 0 0 0 0 0 1 0 0 0
15 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
16 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
17 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
18 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
19 Apartment Auckland Region 665 4 3.63 6.48 595 0.19 207 93 ... 0 0 0 0 0 0 0 1 0 0
20 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
21 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
22 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
23 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
24 Apartment Auckland Region 1051 5+ 3.58 6.85 703 0.46 51 27 ... 0 0 0 0 0 0 0 0 1 0
25 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
26 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
27 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
28 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
29 Apartment Auckland Region 468 NA 3.75 6.11 378 0.27 1275 621 ... 0 0 0 0 0 0 0 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1450 Flat West Coast Region 206 2 3.23 5.30 175 0.22 129 63 ... 0 0 0 1 0 1 0 0 0 0
1451 Flat West Coast Region 206 2 3.23 5.30 175 0.22 129 63 ... 0 0 0 1 0 1 0 0 0 0
1452 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1453 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1454 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1455 Flat West Coast Region 271 3 3.48 5.56 250 0.32 12 9 ... 0 0 0 1 0 0 1 0 0 0
1456 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1457 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1458 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1459 House West Coast Region 226 2 3.23 5.40 200 0.21 165 93 ... 0 0 0 1 0 1 0 0 0 0
1460 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1461 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1462 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1463 House West Coast Region 259 3 3.48 5.53 220 0.23 579 243 ... 0 0 0 1 0 0 1 0 0 0
1464 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1465 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1466 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1467 House West Coast Region 299 4 3.47 5.68 250 0.21 153 51 ... 0 0 0 1 0 0 0 1 0 0
1468 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1469 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1470 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1471 House West Coast Region 273 NA 3.32 5.60 240 0.18 48 9 ... 0 0 0 1 0 0 0 0 0 1
1472 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1473 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1474 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1475 NA West Coast Region 263 NA 2.76 5.52 215 0.36 27 9 ... 0 0 0 1 0 0 0 0 0 1
1476 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0
1477 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0
1478 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0
1479 Room West Coast Region 90 1 3.33 4.50 90 0.00 6 12 ... 0 0 0 1 1 0 0 0 0 0

1480 rows × 52 columns

KNN by Standard Standard Deviation of weekly rent

More linear regression R-squared values, using the Standard Standard Deviation of weekly rent values for KNN analysis, try to use K values of 3, 5, 10, 50, I think k is 5 is more suitable for prediction

In [111]:
#KNN prediction
X = house_rent_text.sd.values
X = np.reshape(X, (len(house_rent_text.sd), 1))
y = house_rent_text.rent_m.values
y = np.reshape(y, (len(house_rent_text.rent_m), 1))
In [121]:
# Fit regression model
x = np.linspace(0, 60, 400)[:, np.newaxis]
n_neighbors = 5

for i, weights in enumerate(['uniform', 'distance']):
    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
    y_hat = knn.fit(X, y).predict(x)
    
    plt.subplot(2, 1, i + 1)
    plt.scatter(X, y, c='k', label='data')
    plt.plot(x, y_hat, c='b', label='prediction')
    plt.axis('tight')
    plt.xlabel('Rent')

    plt.legend(loc='upper left')
    plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))
    plt.subplots_adjust( hspace=0.5)
    
plt.show()

KNN by 5+ Bedroom value

I tried to use different features for KNN analysis, hoping to get some different inspirations.

In [93]:
#KNN prediction
X = house_rent_text.Nb5.values
X = np.reshape(X, (len(house_rent_text.Total), 1))
y = house_rent_text.rent_m.values
y = np.reshape(y, (len(house_rent_text.rent_m), 1))
In [94]:
# Fit regression model
x = np.linspace(0, 10, 100)[:, np.newaxis]
n_neighbors = 2

for i, weights in enumerate(['uniform', 'distance']):
    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
    y_hat = knn.fit(X, y).predict(x)
    
    plt.subplot(2, 1, i + 1)
    plt.scatter(X, y, c='k', label='data')
    plt.plot(x, y_hat, c='b', label='prediction')
    plt.axis('tight')
    plt.xlabel('Rent')

    plt.legend(loc='upper right')
    plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))
    plt.subplots_adjust( hspace=0.5)
    
plt.show()